valuing ipos using pro forma earnings in the prospectus...

Proceedings of Paris Economics, Finance and Business Conference

13 - 15 April 2015, Crowne Plaza Hotel Republique, Paris, France, ISBN: 978-1-922069-73-3

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Valuing Ipos Using Pro Forma Earnings in the Prospectus under Article 11 of Regulation S-X

*

Jerry W. Chen

This study investigates the value relevance of pro forma earnings disclosed in IPO prospectuses under Article 11 of Regulation S-X. We examine the predictive ability for future earnings, the explanatory power for valuing IPOs and the relation with future stock returns of pro forma earnings. We find that pro forma earnings have stronger predictive ability for future earnings and enhance the power of IPO valuation models for non-Internet IPOs. We don’t find similar evidence, however, for Internet IPOs. The lack of evidence for Internet IPOs may be explained by the non-value-relevance of earnings for Internet IPOs documented in the literature. Market efficiency test furthermore reveals that investors completely understand pro forma earnings at the time of IPO.

Keywords: Pro forma earnings, Article 11 of Regulation S-X, IPO valuation, market efficiency JEL classification: G12, G14, G38, M41 Data availability: Data are available from sources identified in the text.

1. Introduction

In theory, there is no difference between valuing initial public offerings (IPOs) and valuing other stocks, using the common approaches of discounted cash flow and price multiples. In practice, because future cash flow is not easy to be estimated for IPOs, practitioners emphasizes the use of price multiple approach using historical accounting numbers such as earnings as proxies for cash flow surrogates and comparable firm multiples such as P/E ratio as proxies for discount factors (Healy and Palepu 2013).1 However, because many IPOs are young growing firms, historical earnings number is of limited use in projecting future earnings or cash flows and thus in general not a reliable measure for valuing IPOs, especially because many firms going public are being valued on the basis of their growth options, not their historical earnings (Ritter and Welch 2002). Kim and Ritter (1999) find only a modest ability to explain the pricing of IPOs using historical earnings multiples. Although it is difficult to come up with accurate valuation measures for IPOs based on historical earnings numbers, prior research provides evidence of improved power of valuation tests using earnings measures with forwarding-looking information. For example, Kim and Ritter (1999) find earnings forecast can enhance the power of valuation tests. However, because earnings forecast figures are not available to market participants at the time of IPO, 2 it is difficult to measure the usefulness of this information for market participants in valuating IPOs.

Jerry W. Chen, Department of Accountancy and Law, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, Email: [email protected] 1 Researchers also use other accounting numbers such as sales or book value of equity as multiples. Earnings

measures, however, are still the most popular. 2 In the U.S., SEC mandates that underwriters (and other syndicate members) can only comment on the

valuation and provide earnings estimates on the new company after the “quiet period” (See Ellis et al. 1999).

mailto:[email protected]



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Our study seeks to bridge this gap in the literature. Our first objective is to identify earnings

numbers that contain forward-looking information and are available at the time of IPO. We find that

pro forma earnings disclosed in the IPO prospectus meet these requirements. In the United States,

pro forma earnings disclosed by companies in the IPO prospectus are subject to the regulation of Article 11

of Regulation S-X. Under this regulation, companies that have experienced or are about to experience material

transactions are required to prepare pro forma financial statements to illustrate how the transactions would

have affected historical financial statements if they had happened at an earlier time. The objective of pro forma

financial statements is to assist investors in analysing a company’s future prospects as it illustrates the possible

scope of the change in the firm’s historical financial statements. The difference between the historical and pro

forma result reflects the potential effects expected to result from the transaction and therefore can assist

investors to have a clearer understanding about the firm’s future prospects.

Next we examine whether pro forma earnings assist the valuation process for IPOs by implementing

three sets of tests. The first set of tests examines the ability of pro forma earnings to predict future earnings by

adopting the earnings persistent model (predictive ability tests). The second set of tests examines whether pro

forma earnings enhance the valuation power in the IPO process by comparing the ability of historical and pro

forma earnings to explain the IPO offer and first-day market price (IPO valuation tests). The final set of tests

examines whether investors incorporate the information content of pro forma earnings into IPO pricing

procedure completely by investigating the relation between pro forma earnings and post-IPO stock returns

(future return tests).

Our sample is consisted of all IPO firms that disclose pro forma earnings in their prospectuses from 1997

to 2009. We start in 1997 because 1997 is the first year that IPO prospectus, Form S-1, became widely

available from the SEC’s EDGAR site. Because our sample period overlaps with the dot-com bubble period,

we partition our sample into Internet and non-Internet IPOs to address the possible effects of valuation



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difference between these two classes of stocks on our empirical tests.3 The empirical result of predictive ability

tests indicates that pro forma earnings adjustments4 are not significantly related to future earnings for Internet

IPOs, suggesting that pro forma earnings adjustments do not contain forward-looking information. This result

may be expected given the evidence in the literature that earnings of Internet IPOs contain limited information

for future earnings (See Bartov et al. 2002). By contrast, pro forma earnings adjustments are positively and

significantly related to future earnings for non-Internet IPOs.

The result of valuation tests furthermore indicates that pro forma earnings do not perform better than

historical earnings in valuing IPOs for Internet IPOs. We do not find (1) improved explanatory power of

valuation tests on pro forma earnings relative to historical earnings or (2) significantly related pro forma

earnings adjustments, for valuing Internet IPOs. This result is consistent with the literature that investors do not

price earnings of Internet IPOs. On the other hand, we find that pro forma earnings adjustments are positively

and significantly related to the offer and first-day market price for non-Internet IPOs and the adjusted R-square

is improved when pro forma earnings are employed in the valuation model rather than historical earnings. This

result suggests that pro forma earnings are improved valuation measures for non-Internet IPOs.

The first and second sets provide evidence that pro forma earnings provide stronger predictive ability and

improved explanatory power than historical earnings for non-Internet IPOs but do not perform any better than

historical earnings for Internet IPOs. Our final tests examine whether investors incorporate this information

into their IPO pricing set completely. We find that the relations between pro forma earnings adjustments and

post-IPO stock returns are not statistically significant for both Internet and non-Internet IPOs, suggesting that

public investors have completely incorporate pro forma earnings implications into the IPO pricing measures at

the time of IPO.

Our study contributes to the literature in primary three ways. First, to our best knowledge, this is the first

study that examines the characteristics and valuation roles of alternate earnings measures with forward-looking

3 Prior studies find evidence that Internet IPOs experience a significant difference in terms of valuation from non-Internet IPOs. For

example, Bartov et al. (2002) find that historical earnings are not relevant in pricing Internet IPOs, compared with non-Internet IPOs. 4 We calculate pro forma earnings adjustments as pro forma earnings minus historical earnings to capture the incremental information

content of pro forma earnings.



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information that are available at the time of IPO for IPO stocks. We provide strong evidence that pro forma

earnings are better than historical earnings in predicting future earnings and also enhance the power of IPO

valuation tests, for non-Internet IPOs. Our findings complement the evidence on earnings forecast that the

efforts of providing earnings measures with forward-looking information are deemed valuable by market

investors.

Second, our study provides evidence on the usefulness of pro forma earnings disclosed under Regulation

S-X, in the IPO setting. From SEC’s perspective, such evidence is important, as provision of value-relevance

information to assist market participants’ investment decision is the main objective of SEC to regulate the

disclosure of pro forma financial statements in company filings. Thus, this finding has the potential to provide

evidence on the benefit of maintaining and reporting pro forma financial statements according to the Article 11

of Regulation S-X.

Finally, our study adds to the extant literature on how unsophisticated market participants price earnings.

Prior research argues that investors do not fully incorporate earnings information at the time when the

information is available due to a cognitive bias. In contrast to this argument, we find that investors seem to

understand the long-term earnings implications from pro forma earnings adjustments, as evidenced by their

abilities to completely incorporate pro forma earnings adjustments into their initial stock price formation. This

result benefits investors in forming trading strategies. Instead of searching for mispriced IPOs, a better

strategy is perhaps to process earnings information as quickly as possible.

The rest of the paper is organized as follows. Section 2 provides a brief overview of pro forma financial

reporting in IPO prospectus. Section 3 outlines the research design. Section 4 describes the sample and

discusses various descriptive details. Section 5 presents the empirical results and Section 6 concludes.

2. Pro forma financial reporting in IPO prospectus

The process of companies going public in the United States is governed by The Securities Act of 1933.

Firms that decide to issue new equity securities in public markets are required to file an S-1 registration form



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with the SEC. Part 1 of the S-1 form includes a preliminary prospectus that contains audited financial

statements and other information related to the issuance. Regulations S-K and S-X govern the required

disclosures. Among regulations of Regulation S-X, Article 11 requires companies that have experienced or are

about to experience material transactions to disclose pro forma financial information in their filings. The

regulation provides a list of material transactions, including business combination, disposition of a business,

security offering, autonomous transaction, roll-up transaction and etc. SEC also advises that because the list is

not mandatory, managers need to exercise their own judgments in evaluating the materiality of the transaction

and determining whether the disclosure of pro forma financial information will be required (Trautmann et al.

2008).

The objective of pro forma financial information is to provide investors with information about the

continuing impact of a particular transaction by showing how it might have affected historical financial

statements if the transaction had been consummated at an earlier time. Pro forma financial information is

usually presented in the format of a pro forma financial statement, which consists of a pro forma condensed

balance sheet5, pro forma condensed statements of income

6, and accompanying explanatory notes. It is

ordinarily in columnar form showing condensed historical statements, pro forma adjustments7, and the pro

forma results. Appendix A provides two examples of pro forma financial statement in IPO prospectuses.

Appendix A.1 provides pro forma financial statement in the IPO prospectus of Entropic Communication

Inc. The company went public on December 6th

, 2007. Its most recent fiscal year is 2006 and most recent

interim period for the purpose of balance sheet disclosure is September 2007. The material transaction that the

company considers in preparing pro forma financial reporting is a business combination with RF Magic, which

takes place on June 30, 2007. Because the transaction takes place before the most recent interim date, the

5 A pro forma condensed balance sheet shall be filed as of the end of the most recent period for which a consolidated balance sheet is

required unless the transaction is already reflected in such balance sheet. 6 Pro forma condensed statements of income are ordinarily filed only for the most recent fiscal year and for the period from the most

recent fiscal year end to the most recent interim date for which a balance sheet is required. A pro forma condensed statement of

income may be filed for the corresponding interim period of the preceding fiscal year. However, a pro forma condensed statement of

income shall not be filed when the historical income statement reflects the transaction for the entire period. 7 Pro forma adjustments are generally accompanied with explanatory notes that describe the significant assumptions used in

developing and computing the pro forma adjustments



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historical balance sheet has already reflected the effect of the transaction. Therefore, the company only

prepares unaudited pro forma statements of income for the fiscal year 2006 and most recent reporting period

September 2007.

In preparing the pro forma statement of income for the fiscal year 2006, Entropic Communication Inc. first

adds to its own statement of income the statement of income of RF Magic, the target company in the

acquisition. Then the company considers pro forma adjustments related to the acquisition before arriving at the

pro forma loss figure $23,077,000. Those adjustments include (1) an increase of cost of net revenues by

$7,709,000 for amortization of developed technology and the inventory step-up from January 1, 2006 over

their estimated useful lives, (2) an increase of amortization of purchased intangible assets from January 1, 2006

over their useful lives by $3,533,000, (3) an increase of stock-based compensation expense by $3,337,000

allocated to cost of net revenues, research and development expense, sales and marketing expense, and general

and administrative expense, and (4) an increase of interest income by $30,000 to reflect the fair value of RF

Magic’s debt that was assumed in the acquisition and related effective interest rate. We define the difference

between pro forma net loss ($23,077,000) and net loss ($7,051,000) as pro forma earnings adjustment, which is

-$16,026,000, indicating the RF Magic acquisition has an income-decreasing effect on Entropic’s operating

performance for fiscal year 2006 if the acquisition takes place on 1 January 2006. More importantly, this

income-decreasing effect is a continuing effect of the RF Magic acquisition and expected to exist for post-

acquisition years.

Appendix A.2 presents pro forma financial statement for Addus HomeCare Corporation. The company

went public on October 27th

, 2009. Its most recent fiscal year is ended December 31, 2008 and most recent

interim period for the purpose of balance sheet disclosure is ended June 30, 2009. The material transaction that

the company considers in preparing pro forma financial reporting is the initial public offering and relevant

transactions. The material transaction takes place after the most recent interim period and the company

includes an unaudited pro forma balance sheet for the most recent interim period as of June 30, 2009 and



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unaudited pro forma statements of income for the most recent fiscal year ended December 31, 2008 and

interim period ended June 30, 2009.

In preparing the pro forma statement of income for the fiscal year 2008, Addus makes a series of

adjustments to its historical statement of income. The company reduces the general and administrative

expenses by $350,000 due to the related fees and expenses associated with the IPO offering and the new credit

facility, increases the interest expense by $2,232,000, and increases the income tax expense by $989,000. As a

result, the net effect on historical net earnings is an increase of $1,593,000 if the IPO offerings and new credit

facility takes place on January 1, 2008. Similar as in Appendix A.1, we define pro forma earnings adjustment

as the difference between pro forma net income ($5,616,000) and net income ($4,023,000), which is

$1,593,000, indicating the initial public offering of Addus has an income-increasing effect on the firm’s

operating performance for fiscal year 2008 assuming the initial public offering takes place on 1 January 2008.

This income-increasing effect is a continuing effect of the Addus IPO and expected to exist for post-IPO years.

3 Research Design

The principal research goal in this study is to examine whether pro forma financial information

disclosed in the prospectus under Article 11 of Regulation S-X assists investors in valuing IPOs. We focus on

pro forma earnings before extraordinary items and discontinued operations (PFIBC) for the most recent fiscal

year disclosed in the IPO prospectus as our pro forma financial information variable and implement three sets

of tests.

We focus on pro forma earnings because it has some desirable features than other pro forma accounting

numbers. First, all pro forma IPOs are required to prepare pro forma income statement but not pro forma

balance sheet for the most recent fiscal year before IPO.8 Compared with pro forma balance sheet measures,

pro forma income statement measures would be more consistent across the sample. Thus, as the bottom line

8 A pro forma balance sheet won’t be filed if the material transaction is already reflected in such balance sheet. As illustrated in

Appendix A.1, because the RF Magic acquisition takes place before the most recent interim date September 30, 2007, historical

balance sheets as of December 31, 2006 and as of September 30, 2007 have already reflected the effect of the acquisition. Therefore,

the company doesn’t file pro forma balance sheets as of December 31, 2006 and as of September 30, 2007.



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measure of pro forma income statement, pro forma earnings provide a comprehensive picture of pro forma

income statement information. Second, as required by SEC Regulation S-X Rule 11-02, compared with pro

forma balance sheet measures, pro forma earnings are prepared reflecting continuing effect of material

transaction therefore its forward-looking nature can be easy to calibrate by examining its predictive ability for

future earnings. Finally, prior studies find that earnings measures perform better than balance sheet measures in

terms of valuation accuracy (See Liu et al. 2002). Thus, pro forma earnings will be a more powerful variable in

verifying the role of pro forma financial information in valuing IPOs, if there is any.

3.1. Predictive Ability Tests

We first examine whether pro forma earnings predict future earnings better than historical earnings. If

pro forma earnings truly reflect recurring effect of material transactions, then they should predict future

earnings better than historical earnings. In order to investigate this question, we regress post-IPO-year three-

year average earnings separately on pro forma and historical earnings9:

FIBC =α0 + α1IBC +Industry &Year Dummies + ε (1)

FIBC =α0 + α1PFIBC +Industry &Year Dummies + ε (2)

where

FIBC = the average earnings before extraordinary items and discontinued operations (annual Compustat data

item 123) for the three fiscal years after the IPO year;

IBC = historical earnings before extraordinary items and discontinued operations (annual Compustat data item

123) for the most recent fiscal year disclosed in the IPO prospectus;

PFIBC = pro forma earnings before extraordinary items and discontinued operations for the most recent fiscal

year disclosed in the IPO prospectus.

9 Prior studies also advise the use of future cash flows as the dependent variables. However, the accrual accounting system and the

nature of pro forma earnings allow a more powerful test using future earnings. For example, if pro forma earnings contain

information related to non-cash items such as depreciation expenses, it will be predictive of future earnings but not cash flows.



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To mitigate heteroskedasticity problem and limit the influence of outliers, we scale all variables by

average total assets and winsorize at the 1% and 99% levels. The adjusted-R2 values of Models (1) and (2)

compare the predictive ability of pro forma and historical earnings. Our prediction is the adjusted-R2 of Model

(2) is larger than that of Model (1).

We also estimate the following model in order to directly measure the incremental predictive ability of

pro forma earnings relative to historical earnings:

FIBC = α0 + α1IBC +α2 PFIBCADJ + Industry &Year Dummies + ε (3)

where

PFIBCADJ = the difference between pro forma and historical earnings before extraordinary items and

discontinued operations (annual Compustat data item 123) for the most recent fiscal year

disclosed in the IPO prospectus.

Model (3) provides direct empirical evidence about whether pro forma earnings predict future earnings

better than historical earnings. Our prediction is that the coefficient on PFIBCADJ, α2 is positive and

significant.

3.2. IPO Valuation Tests

We next examine whether pro forma earnings are more powerful than historical earnings in valuating

IPOs. If the stock market anticipates the recurring earnings information of a firm’s pro forma earnings then the

pricing of IPOs should reflect this information. In order to investigate this question, we regress the IPO pricing

measures on pro forma and historical earnings. We use two measures for IPO pricing, the offer price and first-

day end market price. We also include some control variables that are specific to the IPO setting. We control

for book value of equity for the year just prior to IPO because book value of equity is significantly associated

with IPO equity value (Klein 1996). We control for ownership retention because prior studies find that greater

relative insider ownership is a positive signal to investors indicating that the IPO is not simply a vehicle for the

founders to bail out (Leland and Pyle 1977; Fan 2007). We control for underwriter reputation because firms



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expecting relatively high growth or low risk in earnings and/or revenues will signal this favorable information

to outside investors by selecting a more ―prestigious‖ underwriter (Beatty and Ritter 1986; Carter et al. 1998;

Klein 1996). We control for venture capital backing because similarly prestige venture backing sends signal to

the market (Barry et al. 1990; Megginson and Weiss 1991). We control for price update when the dependent

variable is the first-day end market price because high price update will lead to high IPO pricing (Hanley

1993). We therefore, estimate the following models:

OFFER (PRC) = ß0 + ß1IBC + ß2CEQ + ß3RENTENTION + ß4HIGHUW + ß5VC + ß6UPDATE + Industry

&Year Dummies + ε (4)

OFFER (PRC) = ß0 + ß1PFIBC + ß2CEQ + ß3RETENTION + ß4HIGHUW + ß5VC + ß6UPDATE + Industry &

Year Dummies + ε (5)

where

OFFER (PRC) = the IPO offering (first-day end market) price;

IBC = historical earnings before extraordinary items and discontinued operations (annual Compustat data item

123) for the most recent fiscal year disclosed in the IPO prospectus;

PFIBC = pro forma earnings before extraordinary items and discontinued operations for the most recent fiscal

year disclosed in the IPO prospectus;

CEQ = book value of total equity (annual Compustat data item 60) at the year-end before IPO;

RETENTION = the number of shares held by shareholders prior to IPO divided by total shares outstanding

after IPO;

HIGHUW = an indicator variable that is equal to one if the underwriter prestige ranking based on Loughran

and Ritter (2004) is larger than 8, and zero otherwise;

VC = an indicator variable that is equal to one if the IPO firm is venture capital backing, and zero otherwise;

UPDATE = the difference between the mid-range of preliminary price and offer price, scaled by mid-range of

preliminary price.



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To mitigate heteroskedasticity problem and limit the influence of outliers, we scale IBC, PFIBC, CEQ

by total number of shares outstanding after the IPO and winsorize all continuous variables at the 1% and 99%

levels. Consistent with treatment in the literature, we separate positive IBC from negative IBC to address the

different value-relevant nature of different signs of earnings (See Bartov et al. 2002; Aggarwal et al.2009). The

adjusted-R2 values of Model (4) and (5) compare the valuation power between historical and pro forma

earnings. Our prediction is the adjusted-R2 of Model (5) is larger than that of Model (4).

Similarly, we also estimate the following regression to directly measure the incremental valuation

power of pro forma earnings relative to historical earnings, ß2:

OFFER (PRC) = ß0 + ß1IBC + ß2PFIBCADJ +ß3CEQ + ß4RENTENTION + ß5HIGHUW + ß6VC + ß7UPDATE

+ Industry &Year Dummies + ε (6)

where

PFIBCADJ = the difference between pro forma and historical earnings before extraordinary items and

discontinued operations (annual Compustat data item 123) for the most recent fiscal year

disclosed in the IPO prospectus.

This model provides direct empirical evidence about whether pro forma earnings predict IPO price

better than historical earnings. Our prediction is that the coefficient on PFIBCADJ, ß2 is positive and

significant.

3.3. Future Return Tests

Finally we examine whether the market is efficient using pro forma earnings to value IPOs. If pro

forma earnings carry the recurring earnings information and the stock market fully anticipates this implication,

then the first day end market price of IPOs should have completely incorporated this information.

Alternatively, if the market reaction is incomplete then, as the earnings information materializes, future stock

returns should respond accordingly. To investigate this question, we use two approaches.



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Our first approach is to regress post-IPO stock returns on pro forma earnings adjustments (PFIBCADJ),

the difference between pro forma and historical earnings (Event-time regression approach). We focus on pro

forma earnings adjustments because it captures the incremental information contained in pro forma earnings

relative to historical earnings. We also include control variables that are previously documented determinants

of post-IPO stock returns. We control for post-IPO market returns, firm size, and book-to-market ratio because

prior studies find that these three factors are largely related to stock returns (Fama and French 1993;

Lakonishok et al. 1994). We control for total accruals for the year before IPO because prior studies find that

investors are likely to misprice total accruals at the time of IPO (Teoh et al. 1998a, 1998b). We control for firm

age because younger firms are more likely to have worse post-IPO stock returns (Ritter 1991). We control for

underwriter reputation because IPOs handled by more prestigious underwriters are expected to experience less

severe underperformance (Beatty and Ritter 1986; Carter et al. 1998). We control for venture capital backing

because post-IPO stock-price performance between venture-backed and nonventure-backed IPOs is expected to

be different (Brav and Gompers 1997; Bergstrom et al. 2006). We control for underpricing, as it is an anomaly

widely documented in the literature (Ritter and Welch 2002). We therefore, estimate the following model:

BHRET = r0 + r1PFIBCADJ + r2BHMKTRET + r3LOGMV + r4LOGBTM + r5TACC + r6LOGAGE +

r7HIGHUW + r8VC + r9UNDERPRICING + Industry &Year Dummies + ε

(7)

where

BHRET = three-year buy-and-hold returns in months 2-37 subsequent to the month of IPO issuance;

BHMKTRET = three-year buy-and-hold market returns in months 2-37 subsequent to the month of IPO

issuance;

LOGMV = the natural logarithm of market value of IPO firm immediately after the offering, calculated as total

number of shares outstanding after the IPO multiplied by the first trading day closing price;

LOGBTM = the natural logarithm of the book value of total equity (annual Compustat data item 60) at year

end before IPO scaled by first-day IPO market value of equity;



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TACC = earnings before extraordinary items (annual Compustat data item 123) less cash flow from operations

(annual Compustat data item 308 – annual Compustat data item 124) for the year before IPO;

LOGAGE = the natural logarithm of one plus years from founding date to IPO date, where founding date of

IPOs are obtained from Jay Ritter’s website;

HIGHUW = an indicator variable that is equal to one if the underwriter prestige ranking based on Loughran

and Ritter (2004) is larger than 8, and zero otherwise.

VC = an indicator variable that is equal to one if the IPO firm is venture capital backing, and zero otherwise;

UNDERPRICING = the difference between the first trading day closing price and final offer price, scaled by

the final offer price.

To mitigate heteroskedasticity problem and limit the influence of outliers, we scale PFIBCADJ and

TACC by average total assets and winsorize all continuous variables at the 1% and 99% levels. The coefficient

on PFIBCADJ provides the measure of market efficiency in using pro forma earnings at the time of IPO. If the

earnings forecasting and valuation tests yield no evidence of mispricing, then I expect r1 to be insignificant.

Alternatively, if the earnings forecasting and valuation tests yield evidence of mispricing, then I expect r1 to be

significant.

As Fama (1998) points out, the buy-and-hold returns, while conceptually more representative of an

investor’s investment experience, do not adequately control for cross- sectional correlation among individual

firms and are, therefore, likely to yield overstated t- statistics in event-time regressions. To address this

concern, our second approach is to measure the post-IPO stock returns of quintile PFIBCADJ portfolios in

calendar time relative to certain asset-pricing model (Fama 1998). Under this approach, quintiles are formed

for each month based on pro forma earnings adjustments. The equal-weighted returns are then computed for

each quintile. To control for the difference in the risk exposure across the quintiles, we regress the monthly

quintile returns (in excess of risk-free rate) on the Carhart (1997) four factors, as in the following equation:

Rpt –Rƒt = ap + bpRMFt + spSMBt +hpHMLt + upUMD +εpt,



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where Rƒt is the 30-day T-bill yield in month t and the Carhart four factors (defined in Table 8) are obtained

from Kenneth French’s website.

The regressions are carried out separately for each quintile. Because the portfolio in any given month

may vary significantly, we estimate the regression using weighted least squares to mitigate heteroscedasticity,

where the weight is the square root of the number of firms in each month (Franks et al. 1991). If the Carhart

model provides a complete description of expected returns, then the intercepts are expected to be 0 if pro forma

earnings adjustments have no effect. Thus, any significant differences between the intercepts of the aggressive

and conservative pro forma earnings adjustments quintiles are attributed to differences in pro forma earnings

adjustments.

4. Sample Selection and Descriptive Evidence

4.1. Sample selection

The initial sample consists of 3,673 domestic U.S. IPOs extracted from the Securities Data Corporation

(SDC) database from 1997 to 2009. We start in 1997 because 1997 is the first year that offering prospectus,

Form S-1, became widely available from the SEC’s EDGAR site, www.sec.gov/edgar.shtml.10

Following prior

studies we exclude certain types of IPOs and certain sectors in order to obtain a more homogeneous sample.

Table 1 summarizes the effects of the sample-selection criteria on the sample size. We eliminate firms that

register with non S-1 forms, reducing the sample size to 2,484 IPOs. We also eliminate firms in financial

services, insurance and real estate industries (SIC codes between 6000 and 6999, inclusive), further reducing

the sample size to 2,129 IPOs. We lose 18 IPOs that are unavailable in the Centre for Research on Security

Prices (CRSP) database, 107 IPOs that are non-ordinary or common shares11

, and 5 IPOs of which offer price

is less than $5, leading to a final sample of 1,999 IPOs. We download 424B form of these IPOs and perform a

10

According to Ljungqvist and Wilhelm (2003), since early May of 1996 all companies, foreign and domestic, are required to file

SEC registration statements, periodic reports, and other forms electronically through EDGAR. Section 5(b) of the Securities Act of

1933 requires issuing firms to file an S-1 registration form with the SEC prior to the sale of securities to the public. The first part of

Form S-1 is the offering prospectus. 11

We do not rely on SDC classification alone for identifying IPOs of ordinary shares since SDC occasionally identifies ADRs as

ordinary shares. We independently verify the share type using CRSP codes.



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detailed examination for pro forma earnings metric. We drop 87 IPOs that have no whole-year pre-IPO

financial statements in the prospectus, 333 IPOs that have no pro forma financial statements and 756 IPOs that

have pro forma earnings adjustments related only to ―below the bottom line‖ items, including pro forma

adjustments related to extraordinary items, discontinued operations, cumulative effect of changes in accounting

principles, dividends on preference shares, and the calculation of weighted average outstanding shares, 22 IPOs

that do not provide detailed reconciliation calculation from historical earnings to pro forma earnings, resulting

in a final sample size of 800 IPOs.

(INSERT TABLE 1 ABOUT HERE)

We extract stock price and return data from CRSP, pre-IPO non-financial statement data from SDC and

from sources identified in the text. We extract pre- and post-IPO financial statement data from Compustat. If

any financial statement data are missing, we try to hand collect them from IPO prospectuses and 10-K forms

from EDGAR website, using the Compustat definitions.

4.2. Distributive Description

Table 2 presents the distribution of both pro forma IPOs (IPOs that disclose pro forma earnings in

prospectus) and total IPOs of our sample by year and industry. Panel A shows that the distribution pattern of

pro forma IPOs is very similar to the one of total IPOs: during 1997-2000 the IPO market was booming,

whereas 2001-2009 is characterized as a cold IPO market. Subperiod 1997-2000 accommodates more than 60

percent of the sample. Panel B presents the distribution by one-digit SIC-code. It shows that pro forma IPOs

are most frequent in the industry of service, similar to total IPOs. Because our IPO sample period includes the

period of 1997-2000, a period labelled as dot-com bubble, we also examine the distribution of pro forma and

total IPOs across Internet and non-Internet industries. We identify Internet IPOs based on the information

available on Professor Jay Ritter’s website.12

Panel C represents the percentage of Internet firms of total firms

12

http://bear.warrington.ufl.edu/ritter/ipodata.htm



16

for both pro forma and total IPOs. We find that Internet pro forma IPOs take 17% of total pro forma IPOs. A

similar percentage is found for total IPOs as well.


Table 3 provides descriptive evidence on the distribution of pro forma IPOs by adjustment category. I

begin the analysis by classifying each pro forma IPO into one of the material transaction categories according

to Rule 11-01 of Regulation S-X.13

Because a single prospectus may contain more than one adjustment

category, we identify 1,126 pro forma adjustments for 800 pro forma IPOs. This suggests that on average there

are 1.41 pro forma adjustments for each pro forma IPO. We also find that among pro forma adjustment

categories, business combination (COMBINATION) is the most popular adjustment, representing almost half

of all categories, followed by adjustments related to the IPO offering (OFFER) and change of corporate tax

status (TAX). The result after we split the pro forma IPO sample into Internet and non-Internet pro forma IPOs

is more or less the same. Both subsample present COMBINATION as the most popular adjustment category

followed by OFFER and TAX. The only difference is that Internet pro forma IPOs are more likely associated

with adjustment category of business combinations, which represents more than 70% of the total adjustment

categories.


5. Empirical Tests

5.1. Descriptive Statistics

As discussed earlier, because our sample period covers a large part of dot-com bubble period, we

partition our sample into two subsamples for Internet and non-Internet IPOs when implementing empirical

analyses. This treatment is necessary especially because prior studies have documented the different valuation

results between Internet and non-Internet companies at the stage of initial public offering. For example, Bartov

et al. (2002) document that investors do not price earnings at the stages of IPOs for Internet firms, possibly

13

The rule provides a list of material transactions, including business combination, disposition of a business, security offering,

autonomous transaction, roll-up transaction and etc.



17

because investors believe earnings of Internet firms provide limited earnings information for the future. Under

such circumstance, it is possible that even though we find an insignificant relation between pro forma earnings

and IPO valuation for Internet IPOs, it is the non-value-relevance nature of earnings that drives the result,

rather than the pro forma earnings itself.

Table 4 presents the descriptive statistics of variables used in our empirical tests for the Internet and

non-Internet pro forma IPOs. Reading across the table, I note three salient points. First, the medians of scaled

pro forma earnings (PFIBC_AT and PFIBC_PS) are smaller than scaled historical earnings (IBC_AT and

IBC_PS) for Internet firms, indicating that pro forma earnings adjustments have income-decreasing effect on

historical earnings. On the other hand, scaled pro forma earnings are larger than scaled historical earnings for

non-Internet firms, suggesting that pro forma earnings adjustments have income-increasing effect on historical

earnings. Second, consistent with findings of prior research, IPOs generally exhibit underpricing followed by

poor post-IPO performance. The underpricing of Internet IPOs is higher than non-Internet IPOs, but exhibit

worse post-IPO performance than non-Internet IPOs. For example, in the post-IPO period the medians of

earnings (FIBC_AT) and returns (BHRET) are both smaller than those of non-Internet IPOs. Finally, both

medians of total accruals for Internet and non-Internet IPOs are negative, suggesting that managers do not

inflate accruals before the IPO date. This contrasts the evidence in prior studies that managers are involved in

earnings management before IPO.




18

5.2. Predictive Ability Tests

Table 5 presents results regarding the relative predictive ability of pro forma and historical earnings.

Columns (1)-(3) estimate Models (1)-(3) described above for Internet IPOs. The results indicate that the

coefficient on pro forma earnings is not statistically significant suggesting that pro forma earnings are not

predictive of future earnings for Internet IPOs. The adjusted-R2 of Model (2) is smaller than that of Model (1),

suggesting that pro forma earnings do not predict future earnings better than historical earnings. This

conclusion is furthermore confirmed by the result of Model (3) in Column (3). As explained above, Model (3)

enables us to directly measure the coefficient on pro forma earnings adjustments, a variable to measure the

incremental predictive ability of pro forma earnings relative to historical earnings. Consistent with the results

from the previous Columns, we find that the coefficient on pro forma earnings adjustments is not statistically

significant suggesting that pro forma earnings do not contain incremental implication for future earnings

compared with historical earnings.

Columns (4)-(6) estimate Models (1)-(3) described above for non-Internet IPOs. We find that the

coefficients on both pro forma and historical earnings are significantly positive in Model (1) and (2),

suggesting that both pro forma and historical earnings are predictive of future earnings. The adjusted-R2 of

Model (2) is much larger than that of Model (1), suggesting that pro forma earnings predict future earnings

much better than historical earnings. The result in Column (3) leads to a similar conclusion. We find that the

coefficient on pro forma earnings adjustments is positive and significant suggesting that pro forma earnings

contain important incremental implication for future earnings compared with historical earnings.

In summary, the results in Table 5 report that the predictive ability of pro forma earnings is stronger

than that of historical earnings for IPOs that do not expertise in Internet industries. The insignificant predictive

ability of pro forma earnings for Internet IPOs may be explained by the general limited ability of earnings in

predicting future earnings for Internet IPOs. Prior studies find that it is difficult to estimate future earnings for

Internet IPOs, possibly because they are of too much uncertainty for operation. Our results confirm this

hypothesis.



19


5.3. IPO Valuation Tests

Table 6 reports results of tests examining the valuation power of pro forma and historical earnings for

IPOs. Panel A and B of Table 6 present the regression results for the offer and first-day market price

respectively. Because the results for offer price and first-day market price are similar for most variables, we

discuss results for first-day market price alone and highlight only the differences for the two dependent

variables.

Columns (1)-(3) of Panel B estimate Models (4)-(6) described above for Internet IPOs. The results

indicate that the coefficients on both historical and pro forma earnings are not statistically significant

suggesting that both earnings metrics are not priced in valuing Internet IPOs. This result is consistent with

prior studies on the limited informativeness of earnings for valuing Internet IPOs. The adjusted-R2 of Model

(5) is more or less the same as that of Model (4), suggesting that pro forma earnings do not predict IPO price

better than historical earnings. This conclusion is furthermore reinforced by the result of Model (6) in Column

(3). As explained above, Model (6) enables us to directly measure the incremental valuation power of pro

forma earnings relative to historical earnings. Consistent with the results from the previous columns, we find

that the coefficient on pro forma earnings adjustments is not statistically significant suggesting that pro forma

earnings do not contain incremental power for valuing Internet IPOs compared with historical earnings.

Columns (4)-(6) of Panel B estimate Models (4)-(6) for non-Internet IPOs. We find that the coefficients

on both positive pro forma and historical earnings are significantly positive in Model (4) and (5), suggesting

that both positive pro forma and historical earnings are relevant in determining the IPO first-day end market

price. In addition, we find that the coefficients on both negative pro forma and historical earnings are

insignificant, suggesting that both negative pro forma and historical earnings are non-value-relevant, consistent

with prior studies (See Bartov et al. 2002). The adjusted-R2 of Model (5) is slightly larger than that of Model

(4), suggesting that pro forma earnings predict IPO first-day end market price better than historical earnings.



20

The results in Column (6) lead to a similar conclusion. We find that the coefficient on pro forma earnings

adjustments is positive and significant suggesting that pro forma earnings contain important valuation

implications compared with historical earnings.

Reviewing the results across all columns in Panel B of Table 6, I note four salient points about the

correlations between control and dependent variables. First, consistent with findings in prior research the

estimates on CEQ are significantly positive, indicating that investors consider bottom-line accounting

measures when expect IPO equity value (See Klein 1996). Second, the estimate on HIGHUW is significantly

positive, indicating that IPOs handled by more prestigious underwriters experienced favorable valuation by the

capital market, possibly because it signals issue quality and therefore reduces the perceived uncertainty about

firm value (Beatty and Ritter 1986; Carter et al. 1998). Third, the estimates on UPDATE are significantly

positive, consistent with the conjecture that IPOs with favorable information revealed prior to the IPO date

receive high market valuation on the first day of public trading (Hanley 1993). Finally, although the estimate

on RETENTION is insignificant in Panel B of Table 6, the estimate is positive and marginally significant in

Panel A, suggesting that IPO value increases when a firm retains more shares (as suggested by signaling

theory) (Leland and Pyle 1977; Fan 2007).

In summary, the results in Table 6 report that pro forma earnings can enhance the valuation power for

non-IPO pricing compared with historical earnings. However, the same conclusion does not hold for Internet

IPOs, consistent with the limited ability of historical earnings for pricing Internet IPOs.


5.4. Future Return Tests

Table 7 reports results of tests examining the relation between pro forma earnings adjustments and

post-IPO stock returns, using event-time regression approach. We find that the coefficient on PFIBCADJ for

both Internet and non-Internet IPOs are not statistically significant suggesting that investors do not misprice

pro forma earnings adjustments at the time of IPO when this measurement is publicly available. In terms of the



21

relations between control variables and BHRET, we find that it varies between Internet and non-Internet IPOs.

Almost all control variables are not significantly related with BHRET for Internet IPOs suggesting that post-

IPO stock returns for Internet IPOs are difficult to estimate. In contrast, control variables display more

traditional relations with BHRET for non-Internet IPOs. First, consistent with prior research, the future market

return is positively and significantly related with future stock returns (Fama and French 1993). Second,

consistent with the well-documented book-to-market anomaly, book-to-market ratio is positively and

significantly related with stock returns (Fama and French 1993; Lakonishok et al. 1994). Third, interestingly,

pre-IPO total accruals are insignificantly correlated with future stock returns. This is inconsistent with Teoh et

al. (1998a, 1998b) finding that IPO firms use pre-IPO accruals to opportunistically overstate earnings but

consistent with Fan (2007) finding that IPO firms use pre-IPO accruals to signal high quality to investors.

Finally, the estimate on UNDERPRICING is marginally negative, consistent with the IPO underpricing

anomaly widely documented in the literature (Ritter and Welch 2002).


Table 8 reports results of tests examining the relation between pro forma earnings adjustments and

post-IPO stock returns, using calendar-time portfolio regression approach. We find that the intercepts from the

Carhart four-factor regression are insignificant for all quintiles for both Internet and non-Internet IPOs. For

both Internet and non-Internet IPOs, the intercepts display a similar movement: increasing from Q1 to Q3 then

decreasing to Q4 and finally increasing significantly to Q5.

We use t-statistics to compare the estimated difference in intercepts between the conservative and

aggressive portfolios. The t-statistics is obtained from an unreported time-series regression of the difference in

returns between the conservative and aggressive portfolios on the factor returns. Even though the intercept of

the aggressive quintile is higher than that of the conservative quintile, the difference is not significant based on

the two-tailed p-value.



22

To summarize, there is no strong evidence that pro forma earnings adjustments and post-IPO stock

returns are significantly correlated. The results are robust across alternative methodologies, which reinforces

our confidence in conclusion that the market is efficient in using pro forma earnings at the time of IPO.

6. Conclusion

In this paper, we investigate whether pro forma earnings disclosed in the IPO prospectus under Article

11 of Regulation S-X is useful in valuing IPOs. In order to investigate this question, we implement three sets

of tests: predictive ability, valuation and post-IPO stock return tests. Our three sets of tests complement each

other to evaluate the value relevance of pro forma earnings compared with historical earnings. A characteristic

of the predictive ability tests is that earnings predictions are important inputs for valuation models (See Ohlson

1995). A characteristic of the valuation tests is to measure the short-term stock price information using the

earnings metrics. A characteristic of post-IPO return tests is to measure the long-term stock price information

using the earnings metrics. The advantage of using three approaches is that the validity of our overall findings

is enhanced if we obtain homogeneous results with each method.

We find that pro forma earnings have different valuation consequences for Internet and non-Internet

IPOs. We find that pro forma earnings do not predict future earnings or IPO issue price for Internet IPOs

suggesting that pro forma earnings do not contain predictive information for Internet IPOs and investors

subsequently do not price this information when valuing IPOs. On the other hand, we find that pro forma

earnings predict future earnings better than historical earnings for non-Internet IPOs. Also investors price this

information when valuing IPOs, as evidenced by the significant valuation power of pro forma earnings

compared with historical earnings. These findings suggest that investors behave as if they incorporate future

earnings information that is captured by pro forma earnings adjustments contained in the IPO prospectus into

their price formation. Does this understanding reflect market efficiency? To address this question I examine the

ability of pro forma earnings adjustments to predict post-IPO stock returns and find that pro forma earnings



23

adjustments are not significantly correlated with post-IPO stock returns. This finding suggests that investors

completely incorporate pro forma earnings adjustments into IPO prices.

Our results contribute to the existing literature in primarily three ways. First, we provide evidence on

the informativeness of pro forma earnings in the IPO prospectus. We find that pro forma earnings adjustments

in the IPO prospectus predict a company’s future earnings performance and are priced by investors. Investors

would be benefited if managers keep reporting pro forma financial statements in their SEC filings. Second, our

findings provide academic support for policy makers in requiring companies to disclose pro forma earnings in

their SEC filings under Article 11 of Regulation S-X. Finally, we find that investors extract the long-term

earnings implications from pro forma earnings adjustments in the IPO prospectus and consequently completely

incorporate pro forma earnings adjustments into their initial stock price formation. This evidence is consistent

with market efficiency hypothesis and benefits investors in forming trading strategies.



24

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26

Appendix A: Examples of pro forma financial statements in IPO prospectuses

A.1. Entropic Communications Inc.

The company went public on December 6th

, 2007. Its IPO prospectus includes unaudited pro forma

condensed consolidated combined statements of operations for the year ended December 31, 2006 and the nine

months ended September 30, 2007, which are based on the historical statements of operations of Entropic and

RF Magic giving effect to their acquisition of RF Magic as if the acquisition had occurred on January 1, 2006.

Entropic Communications, Inc.

Unaudited Pro Forma Condensed Consolidated Combined Statement of Operations

Year Ended December 31, 2006

(in thousands, except per share data)

Entropic RF Magic Notes

Pro Forma

Adjustments

Pro Forma

Combined (unaudited)

Net revenues 41,471 $26,183 — 67,654

Cost of net revenues 31,099 10,579 (a)(c) 7,897 49,575

Gross profit 10,372 15,604 (7,897) 18,079

Operating expenses:

Research and development 11,601 11,182 (c) 1,396 24,179

Sales and marketing 4,112 3,682 (c) 528 8,322

General and administrative 2,192 1,927 (c) 1,225 5,344

Amortization of purchased intangible assets — — (b) 3,533 3,533

Total operating expenses 17,905 16,791 6,682 41,378

Loss from operations (7,533) (1,187) (14,579) (23,299)

Interest income (expense), net 883 (241) (e) 30 672

Loss on fair value of preferred stock warrant liabilities (401) — — (401)

Loss before income taxes (7,051) (1,428) (14,549) (23,028)

Provision for income taxes — (49) — (49)

Net loss (7,051) (1,477) (14,549) (23,077)

Accretion of redeemable convertible preferred stock (126) — — (126)

Net loss attributable to common stockholders (7,177) $(1,477) $(14,549) (23,203)

Net loss per share attributable to common stockholders—basic and diluted (1.66) (2.27)

Weighted average number of shares used to compute loss per share attributable

to common stockholders 4,325 5,898 10,223

Pro forma net loss per common share—basic and diluted (0.20) (0.42)

Weighted average number of shares used to compute pro forma net loss per

share—basic and diluted 35,886 18,941 54,827

Entropic Communications, Inc.

Unaudited Pro Forma Condensed Consolidated Combined Statement of Operations

Nine Months Ended September 30, 2007

(in thousands, except per share data)

Entropic RF Magic Notes

Pro Forma

Adjustments

Pro Forma

Combined (unaudited)

Net revenues 82,377 15,204 — 97,401

Cost of net revenues 54,491 5,444 (a)(c) 500 60,435

Gross profit 27,886 9,580 (500) 36,966

Operating expenses:

Research and development 22,812 6,831 (c) 605 30,248

Sales and marketing 6,642 2,487 (c) 193 9,322

General and administrative 5,104 2,115 (c) 504 7,723

Acquired in-process research and development 21,400 — (d) (21,400) —

Amortization of purchased intangible assets 1,338 — (b) 367 1,705

Total operating expenses 57,296 11,433 (19,731) 48,998

Loss from operations (29,410) (1,853) 19,231 (12,032)



27

Interest income (expense), net (64) (389) (e) 89 (364)

Loss on fair value of preferred stock warrant liabilities (2,006) — — (2,006)

Loss before income taxes (31,480) (2,242) 19,320 (14,402)

Provision for income taxes — (30) (30)

Net loss (31,480) (2,272) 19,320 (14,432)

Accretion of redeemable convertible preferred stock (95) — — (95)

Net loss attributable to common stockholders (31,575) $(2,272) $19,320 (14,527)

Net loss per share attributable to common stockholders—basic and diluted (3.89) (1.21)

Weighted average number of shares used to compute loss per share attributable to

common stockholders 8,113 3,910 12,023

Pro forma net loss per common share—basic and diluted (0.71) (0.26)

Weighted average number of shares used to compute pro forma net loss per

share—basic and diluted 44,363 12,558 56,921

On June 3 , 7, Entropic Communications, Inc. (the ―Company‖) acquired RF Magic, Inc. (―RF

Magic‖), a provider of digital broadcast satellite outdoor unit and silicon television tuner solutions, for

approximately 5,898, shares of the Company’s common stock and 4 ,388, shares of the Company’s

newly created Series D-1, D-2, and D-3 redeemable convertible preferred stock. The shares of Series D-1, D-2

and D-3 redeemable convertible preferred stock are convertible into an aggregate of 13,042,000 shares of the

Company’s common stock. The securities the Company issued or reserved for issuance pursuant to the

acquisition were equal to approximately 34% of the combined company’s fully diluted capitalization. The

acquisition was designed to combine established complementary technologies focused on the rapidly growing

market for connected home entertainment.

In connection with the RF Magic acquisition, the Company entered into an escrow agreement pursuant to

which (i) 884,7 9 shares of the Company’s common stock and (ii) shares of the Company’s Series D

redeemable convertible preferred stock that are convertible into an aggregate of 1,956,315 shares of its

common stock, were deposited into an escrow account to potentially be used to satisfy certain indemnification

claims. On November 14, 2007, the Company and the representative of the former RF Magic stockholders

agreed that 90,059 of these escrowed shares (calculated on an as-converted to common stock basis) would be

distributed to the Company to satisfy an indemnification claim made by the Company relating to the resolution

of a disagreement with EchoStar, and that the remaining shares would thereafter be released to the former RF

Magic stockholders.

The unaudited condensed combined pro forma statements of operations for the year ended December 31,

2006 and the nine months ended September 30, 2007 are based on the historical financial statements of the

Company and RF Magic after giving effect to the Company’s acquisition of RF Magic. o transactions

between RF Magic and the Company have occurred for the periods presented. The unaudited condensed

combined pro forma statements of operations are presented as if the acquisition occurred on January 1, 2006.



28

A.2. Addus HomeCare Corporation

The company went public on October 27th

, 2009. Its IPO prospectus includes an unaudited pro forma

consolidated balance sheet data as of June 30, 2009 and unaudited pro forma consolidated statements of

income for the fiscal year ended December 31, 2008 and the six months ended June 30, 2009.

Addus HomeCare Corporation

Unaudited Pro Forma Consolidated Balance Sheet as of June 30, 2009

(in thousands, except share and per share data)

Actual

Adjustments

Pro Forma

As Adjusted Current assets

Cash 850 — 850

Accounts receivable, net 63,114 — 63,114

Prepaid expenses and other current assets 9,195 — 9,195

Deferred tax assets 4,059 — 4,059

Income taxes receivables 227 — 227

Total current assets 77,445 — 77,445

Property and equipment, net of accumulated depreciation and amortization 3,184 — 3,184

Other assets

Goodwill (1) 48,216 10,810 59,026

Intangibles, net of accumulated amortization 15,059 —- 15,059

Debt issuance costs, net (5) 1,006 (556) 450

Deferred tax assets 1,010 —- 1,010

Total other assets 65,291 10,254 75,545

Total assets 145,920 10,254 156,174

Current liabilities

Accounts payable 3,381 —- 3,381

Accrued expenses (1) 28,859 85 28,944

Current maturities of long-term debt (3) 10,139 (2,286) 7,853

Deferred revenue 2,033 —- 2,033

Total current liabilities 44,412 (2,201) 42,211

Preferred stock dividends, undeclared subject to payment on conversion to

common stock 11,506 (11,506) —

Long-term debt, less current maturities (3) 54,275 (18,340) 35,935

Total liabilities 110,193 (32,047) 78,146

Commitments, contingencies and other matters

Stockholders’ equity

Common stock – $.001 par value; 40,000,000 authorized and

1,019,250 and 10,496,251 issued and outstanding shares actual and pro

forma as adjusted, as of June 30, 2009, respectively 1 9 10

Preferred stock – $.001 par value; 100,000 and 10,000,000 authorized

and 37,750 and zero issued and outstanding shares actual and pro

forma as adjusted, as of June 30, 2009, respectively 37,750 (37,750) -—

Preferred stock dividends, undeclared subject to payment on

conversion to common stock (11,506) 11,506 —-

Additional paid-in capital (4) 1,569 76,449 78,018

Retained earnings (4) 7,913 (7,913) —-

Total Stockholder’s equity (4) 35,727 42,301 78,028

Total liability and stockholders’ equity 145,920 10,254 156,174



29


Unaudited Pro Forma Consolidated Statements of Income

for the Calendar Year Ended December 31, 2008


Actual Adjustments

Pro Forma

As Adjusted Net service revenues 236,306 — 236,306

Cost of service revenues 167,254 — 167,254

Gross profit 69,052 — 69,052

General and administrative expenses (1)(2) 52,112 (350) 51,762

Depreciation and amortization 6,092 — 6,092

Total operating expenses 58,204 (350) 57,854

Operating income 10,848 350 11,198

Interest expense (5) (5,806) 2,232 (3,574)

Interest and other income 51 — 51

Income from operations before taxes 5,093 2,582 7,615

Income tax expense (6) 1,070 989 2,059

Net income 4,023 1,593 5,616

Less: preferred stock dividends, undeclared subject to payment upon

conversion (4,270) 4,270 —

Net income (loss) attributable to common shareholders (247) 5,863 5,616

Net income per share of common stock:

Basic (0.24) 0.54

Diluted (0.24) 0.53

Weighted average number of shares outstanding: (7)

Basic 1,019,250 10,496,251

Diluted 1,019,250 10,528,979


Unaudited Pro Forma Consolidated Statements of Income

for the Six Months Ended June 30, 2009


Actual Adjustments

Pro Forma

As Adjusted Net service revenues 126,805 — 126,805

Cost of service revenues 89,440 — 89,440

Gross profit 37,365 — 37,365

General and administrative expenses (1)(2) 27,983 (176) 27,807

Depreciation and amortization 2,444 — 2,444

Total operating expenses 30,427 (176) 30,251

Operating income 6,938 176 7,114

Interest expense (5) (2,180) 881 (1,299)

Interest and other income 12 — 12

Income from operations before taxes 4,770 1,057 5,827

Income tax expense (6) 1,474 405 1,879

Net income 3,296 652 3,948

Less: preferred stock dividends, undeclared subject to payment upon

conversion (2,284) 2,284 —

Net income (loss) attributable to common shareholders 1,012 2,936 3,948

Net income per share of common stock:

Basic 0.99 0.38

Diluted 0.63 0.37

Weighted average number of shares outstanding: (7)

Basic 1,019,250 10,496,251

Diluted 5,203,203 10,529,341



30

The unaudited pro forma consolidated balance sheet data as of June 30, 2009 give effect to this offering

and the payment of related fees and expenses and the following transactions, in each case, as if each such

transaction took place on June 30, 2009: (1) the incurrence of $32.2 million of indebtedness under our new

credit facility, the payment of $0.4 million of related fees and expenses and the simultaneous repayment of

$60.1 million of indebtedness under our existing credit facility (the amount outstanding on June 30, 2009); (2)

the conversion of all outstanding shares of our series A preferred stock into an aggregate 4,077,000 shares of

common stock at a ratio of 1:108 prior to the completion of this offering, the payment of $4.1 million in

respect of accrued and unpaid dividends on such shares and $7.4 million aggregate principal amount of the

dividend notes to be outstanding immediately following the completion of this offering; (3) the payment to the

Eos Funds or their designee(s) of a $1.5 million one-time consent fee in connection with this offering, or the

Sponsor Transaction; (4) the payment of $1.1 million to the Chairman of Addus HealthCare pursuant to his

separation and general release agreement, or the Separation Transaction; and (5) the payment of $12.4 million

to our Chairman of the Board, President and Chief Executive Officer, the Chairman of Addus HealthCare and

certain of our other existing stockholders, pursuant to a contingent payment agreement entered into in

connection with our acquisition of Addus HealthCare, or the Contingent Payment Transaction.

The unaudited pro forma consolidated statements of income for the fiscal year ended December 31, 2008

and the six months ended June 30, 2009 give effect to the following transactions, in each case, as if each such

transaction took place on January 1, 2008: (1) this offering and the payment of related fees and expenses; (2)

the incurrence of $24.4 million of indebtedness under a new credit facility that we intend to enter into at the

completion of this offering, the simultaneous repayment of $53.8 million of indebtedness under our existing

credit facility (the amount outstanding on January 1, 2008), and the payment of related fees and expenses; (3)

the conversion of all outstanding shares of our series A preferred stock into an aggregate of 4,077,000 shares of

common stock at a ratio of 1:108 prior to the completion of this offering, the payment of $4.1 million in

respect of accrued and unpaid dividends on such shares and $0.9 million aggregate principal amount of the

dividend notes to be outstanding immediately following the completion of this offering; and (4) the elimination

of fees payable to an affiliate of the Eos Funds under the management consulting agreement between Addus

HealthCare and that entity, which will terminate prior to the completion of this offering pursuant to an

agreement between Addus HealthCare and that affiliate of the Eos Funds.



31

Table 1

Sample selection process

Total number of U.S. Domestic IPOs between 1997 and 2009 available from the Securities

Data Corporation (SDC) 3,673

Less:

Obs with missing CUSIPS (40)

Obs with multiple entries (12)

IPOs with non Form S-1 form registers (1,137)

Financial services, insurance, and real estate firms (355)

No matching firms in CRSP database (8)

Listed date larger than 10 days (10)

Non-ordinary stock (107)

IPOs with offer price less than $5 (5)

S-1 IPOs by US non-financial firms listed on NYSE, NASDAQ, or AMEX 1,999

Less:

No whole-year financial statement data in prospectus (87)

IPO sample for the period 1997 to 2009

Less:

No pro forma financial statements in prospectus (334)

Pro forma earnings adjustments related to ―below the bottom line‖ items (756)

Pro forma earnings adjustments without reporting specific adjustments used to calculate

the pro forma number (22)

Final sample of pro forma IPOs before restrictions on firm-specific data 800

Note: Financial services, insurance, and real estate firms are firms with SIC codes in the range 6000-6999.

Non-ordinary/common shares issues are identified based on CRSP share code (not equal to 10 and 11).

Examples of pro forma earnings adjustments related to ―below the bottom line‖ items include, e.g. pro forma

adjustments related to extraordinary items, discontinued operations, cumulative effect of changes in accounting

principles, dividends on preference shares, and the calculation of weighted average outstanding shares.



32

Table 2

Distribution of pro forma and total IPOs by year and industry

Panel A: Distribution by year

Issuing Year Number of pro forma IPOs Percent (%) Number of total IPOs Percent (%)

1997 172 21.5 317 16.6

1998 107 13.4 195 10.2

1999 134 16.8 393 20.6

2000 108 13.5 306 16.0

2001 29 3.6 58 3.0

2002 21 2.6 51 2.7

2003 23 2.9 47 2.5

2004 46 5.8 134 7.0

2005 59 7.4 118 6.2

2006 48 6.0 116 6.1

2007 31 3.9 121 6.3

2008 7 0.9 17 0.9

2009 15 1.9 39 2.0

800 100 1,912 100

Panel B: Distribution by SIC code

SIC code Number of pro forma IPOs Percent

(%) Number of total IPOs

Percent

(%)

1-1999 55 6.9 68 3.6

2000-2999 86 10.8 236 12.3

3000-3999 148 18.5 460 24.1

4000-4999 97 12.1 183 9.6

5000-5999 94 11.8 169 8.8

7000-7999 250 31.3 637 33.3

8000-8999 70 8.8 158 8.3

9000-9999 0 0.0 1 0.1

800 100 1,912 100

Panel C: Distribution by Internet segment

Industry Segment Frequency Percent (%) Frequency Percent (%)

Internet 138 17.3 442 23.1

Non-Internet 662 82.8 1,470 76.9

800 100 1,912 100

Note: SIC codes 1-1999 = Mineral and construction industries; SIC codes 2000-2999 = Manufacturing: food,

tobacco, textile, lumber, furniture, paper, printing, chemicals, and petroleum; SIC codes 3000-3999 =

Manufacturing: rubber, leather, stone, metal, machinery, electronic equipment, transportation equipment, etc.;

SIC codes 4000-4999 = Transportation, communications, and utilities; SIC codes 5000-5999 = Wholesale

trade (durable and non-durable) and retail trade (building materials, general merchandise, food, automotive,

apparel); SIC codes 7000-7999 = Service Industries: hotels, personal services, business services, automotive



33

repair, motion pictures, amusement and recreation services; SIC codes 8000-8999 = Service Industries: health,

legal, educational, social, museums, engineering, accounting, management, etc.



34

Table 3

Classification of pro forma IPOs by adjustment category

Total IPOs Internet IPOs Non-Internet IPOs

Adjustment Category Frequency Percent

(%) Frequency

Percent

(%) Frequency

Percent

(%)

COMBINATION 486 43.2 115 74.7 371 38.2

OFFER 284 25.2 17 11.0 267 27.5

TAX 138 12.3 11 7.1 127 13.1

RECAP 94 8.3 3 1.9 91 9.4

REORG 42 3.7 3 1.9 39 4.0

DISPOSE 29 2.6 4 2.6 25 2.6

STAND 20 1.8 0 0.0 20 2.1

ROLLUP 17 1.5 0 0.0 17 1.7

OTHER 16 1.4 1 0.6 15 1.5

Total adjustment 1,126 100 154 100 972 100

Total pro forma IPOs 800 138 662

Average adjustments per pro

forma IPO 1.41 1.12 1.47

Note: COMBINATION = Adjustment giving effect to business combinations; OFFER = Adjustment giving

effect to the sale of IPO and the application of net proceeds; TAX = change of income tax provision due to

change of company’s corporate tax status; RECAP = Adjustment giving effect to recapitalization activity;

REORG = Adjustment giving effect to reorganization activity; DISPOSE = Adjustment giving effect to

disposition of a significant part of a business; STAND = Adjustment reflecting operation and financial position

of the company as an autonomous entity; ROLLUP =Adjustment giving effect to a roll-up transaction;

OTHER=All other adjustments.



35

Table 4

Descriptive Statistics of Selected Variables

Note: PFIBC_AT(PS)= Pro forma income before extraordinary items and discontinued operations for the most

recent year prior to IPO scaled by average total assets (annual Compustat data item 6) (or shares outstanding

after IPO). IBC_AT(PS)= Historical income before extraordinary items and discontinued operations (annual

Compustat data item 123) for the most recent year prior to IPO scaled by average total assets (or shares

outstanding after IPO). PFIBCADJ_AT(PS)= Pro forma income before extraordinary items minus historical

income before extraordinary items for the most recent year prior to IPO scaled by average total assets (or

shares outstanding after IPO). FIBC_AT = Average income before extraordinary items and discontinued

operations (annual Compustat data item 123) for the three years after the IPO year. OFFER = IPO offering

price. PRC = IPO first day end market price. BHRET = three-year buy-and-hold returns in months 2-37

subsequent to the month of IPO issuance. CEQ_PS = book value of total equity (annual Compustat data item

60) at the year-end before IPO scaled by shares outstanding after IPO. RETENTION = the number of shares

held by shareholders prior to IPO divided by total shares outstanding after IPO. HIGHUW = an indicator

variable that is equal to one if the underwriter prestige ranking based on Loughran and Ritter (2004) is larger

than 8, and zero otherwise. VC = an indicator variables that is equal to one if the IPO firm is venture capital

backing, and zero otherwise. UPDATE = the difference between the mid-range of preliminary price and offer

price, scaled by mid-range of preliminary price. BHMKTRET = three-year buy-and-hold market returns in

Internet IPOs Non-Internet IPOs

N Mean Median Std. Dev. N Mean Median Std. Dev.

Earnings Variables

PFIBC_AT 138 -6.462 -0.851 44.293 662 -0.075 0.036 0.857

PFIBC_PS 138 -0.669 -0.473 1.238 662 0.065 0.189 2.043

IBC_AT 138 -0.779 -0.526 1.390 662 -0.040 0.022 0.422

IBC_PS 138 -0.369 -0.226 0.718 662 0.110 0.137 2.304

Dependent Variables

FIBC_AT 114 -0.479 -0.333 0.669 613 -0.032 0.020 0.182

OFFER 138 14.616 14.000 4.984 662 14.336 14.000 4.671

PRC 138 26.834 19.625 19.887 662 16.836 15.408 7.922

BHRET 138 -0.561 -0.945 0.933 662 -0.018 -0.316 1.148

Control Variables

CEQ_PS 138 0.314 -0.061 2.519 662 2.653 0.625 10.625

RENTENTION 138 0.778 0.824 0.182 662 0.681 0.729 0.215

HIGHUW 138 0.848 1.000 0.360 662 0.761 1.000 0.427

VC 138 0.674 1.000 0.470 662 0.251 0.000 0.434

UPDATE 138 0.210 0.174 0.365 662 -0.015 0.000 0.204

BHMKTRET 138 -0.140 -0.187 0.216 662 0.184 0.168 0.343

LOGMV 138 6.359 6.351 1.107 662 5.752 5.717 1.178

LOGBTM 138 0.005 -0.002 0.102 662 0.099 0.043 0.285

UNDERPRICING 138 0.711 0.397 0.819 662 0.156 0.089 0.279

LOGAGE 138 1.815 1.609 0.704 662 2.769 2.708 1.043

TACC_AT 138 -0.319 -0.223 0.442 662 -0.082 -0.063 0.233



36

months 2-37 subsequent to the month of IPO issuance. LOGMV = the natural logarithm of market value of IPO

firm immediately after the offering, calculated as total number of shares outstanding after the IPO multiplied

by the first trading day closing price. LOGBTM = the natural logarithm of the book value of total equity

(annual Compustat data item 60) at year end before IPO scaled by first-day IPO market value of equity.

UNDERPRICING = the difference between the first-day market price and final offer price, scaled by the final

offer price. LOGAGE = the natural logarithm of one plus years from founding date to IPO date, where

founding date of IPOs are obtained from Jay Ritter’s website. TACC_AT = earnings before extraordinary items

and discontinued operations (annual Compustat data item 123) less cash flow from operations (annual

Compustat data item 308 – annual Compustat data item 124) for the most recent year before IPO.



37

Table 5

Predictive Ability Tests

Internet IPOs Non-Internet IPOs Variable (1) (2) (3) (4) (5) (6)

Intercept 0.290 0.349 0.289 0.057 0.059 0.059

(1.14) (1.40) (1.14) (2.46)** (2.54)** (2.57)**

IBC_AT 0.092 0.090 0.265 0.287

(1.55) (1.59) (8.17)*** (11.86)***

PFIBC_AT 0.002 0.253

(1.17) (12.99)***

PFIBCADJ_AT 0.001 0.223

(0.68) (5.20)***

Industry & Year

Dummies

Included Included Included Included Included Included

No of Obs. 114 114 114 613 613 613

Adj. R2 0.1320 0.1075 0.1242 0.3553 0.4087 0.4047

Note: The dependent variable is FIBC_AT. See Table 4 for variable definitions. All variables are windorized at

1 and 99% levels. T-statistics are reported using heteroskedasticity-corrected standard errors based on White

(1980). *, **, *** denote significance at the 10 percent, 5 percent, and 1 percent levels (for a two-sided test),

respectively.



38

Table 6

IPO Pricing Tests

Panel A: Regression for OFFER


Intercept -1.221 -2.163 -1.850 8.443 8.274 8.475

(-0.37) (-0.62) (-0.56) (8.04)*** (8.21)*** (8.15)***

POSIBC_PS 0.033 0.180 1.023 1.026

(0.02) (0.12) (3.92)*** (4.00)***

NEGIBC_PS -0.364 -0.554 0.083 0.095

(-0.41) (-0.60) (0.79) (0.84)

POSPFIBC_PS -0.137 1.536

(-0.11) (5.84)***

NEGPFIBC_PS -0.386 0.025

(-1.61) (0.21)

PFIBCADJ_PS -0.588 0.872

(-1.52) (2.12)**

CEQ_PS 0.272 0.343 0.301 0.035 0.021 0.037

(1.57) (2.19)** (1.75)* (1.15) (0.81) (1.26)

RETENTION 8.322 9.294 9.034 0.829 1.549 1.434

(3.13)*** (3.43)*** (3.42)*** (0.90) (1.88)* (1.75)*

HIGHUW 4.608 4.497 4.516 3.593 3.538 3.551

(5.06)*** (5.01)*** (5.02)*** (10.01)*** (9.99)*** (9.93)***

VC -2.673 -2.503 -2.503 -1.524 -1.480 -1.496

(-2.19)** (-2.03)** (-1.97)* (-3.59)*** (-3.46)*** (-3.53)***

Industry & Year

Dummies


No of Obs. 114 114 114 613 613 613

Adj. R2 0.1873 0.1845 0.1854 0.2415 0.2605 0.2521

Note: See Table 4 for variable definitions. All continuous variables are windorized at 1 and 99% levels. T-

statistics are reported using heteroskedasticity-corrected standard errors based on White (1980). *, **, ***

denote significance at the 10 percent, 5 percent, and 1 percent levels (for a two-sided test), respectively.



39

Table 6

IPO Pricing Tests

Panel B: Regression for PRC


Intercept 10.862 12.735 14.013 9.930 9.959 9.940

(1.44) (1.55) (1.72)* (8.75)*** (8.91)*** (8.80)***

POSIBC_PS 6.979 6.489 0.503 0.704

(1.61) (1.55) (2.39)** (2.87)***

NEGIBC_PS -0.040 0.674 0.109 0.148

(-0.02) (0.26) (1.04) (1.39)

POSPFIBC_PS 6.445 0.813

(1.63) (2.84)***

NEGPFIBC_PS 0.315 0.134

(0.26) (1.25)

PFIBCADJ_PS 2.312 0.614

(1.25) (1.81)*

CEQ_PS -0.040 -0.022 -0.165 0.062 0.067 0.070

(-0.09) (-0.05) (-0.37) (2.04)** (2.25)** (2.30)**

RETENTION 11.735 9.635 8.734 1.458 1.351 1.367

(1.91)* (1.37) (1.26) (1.61) (1.48) (1.51)

HIGHUW 5.333 5.451 5.518 3.909 3.886 3.863

(2.07)** (2.11)** (2.14)** (9.38)*** (9.31)*** (9.29)***

VC -3.830 -3.832 -4.429 0.333 0.358 0.390

(-1.03) (-1.01) (-1.20) (0.61) (0.65) (0.71)

UPDATE 37.664 37.954 38.337 24.490 24.436 24.399

(10.24)*** (10.48)*** (10.38)*** (17.58)*** (17.39)*** (17.48)***

Industry & Year

Dummies


No of Obs. 114 114 114 613 613 613

Adj. R2 0.5631 0.5635 0.5649 0.5699 0.5710 0.5716

Note: See Table 4 for variable definitions. All continuous variables are windorized at 1 and 99% levels. T-

statistics are reported using heteroskedasticity-corrected standard errors based on White (1980). *, **, ***

denote significance at the 10 percent, 5 percent, and 1 percent levels (for a two-sided test), respectively.



40

Table 7

Future Return Tests: Event Time Regression

Variable Internet IPOs Non-Internet IPOs

Intercept 0.089 -0.301

(0.08) (-0.91)

PFIBCADJ_AT 0.003 -0.053

(1.07) (-0.22)

BHMKTRET 1.452 1.177

(1.46) (3.74)***

LOGMV 0.015 0.018

(0.14) (0.43)

LOGBTM -0.168 0.318

(-0.28) (2.20)**

TACC_AT 0.024 0.017

(0.17) (0.09)

UNDERPRICING -0.115 -0.264

(-1.17) (-1.92)*

LOGAGE 0.107 0.018

(0.99) (0.45)

HIGHUW 0.289 0.096

(1.97)* (0.94)

VC 0.027 0.096

(0.25) (0.91)

Industry & Year Dummies Included Included

No of Obs. 114 613

Adj. R2 0.2833 0.0876

Note: The dependent variable is BHRET. See Table 4 for variable definitions. All continuous variables are

windorized at 1 and 99% levels. T-statistics are reported using heteroskedasticity-corrected standard errors

based on White (1980). *, **, *** denote significance at the 10 percent, 5 percent, and 1 percent levels (for a

two-sided test), respectively.



41

Table 8

Future Return Tests: Calendar Time Portfolio Regression

Internet IPOs Non-Internet IPOs

Variable Q1 Q2 Q3 Q4 Q5 Q1 Q2 Q3 Q4 Q5

ap(Intercept) 0.005 0.005 0.012 0.002 0.016 0.001 -0.002 0.003 -0.001 0.001

(0.19) (0.23) (0.56) (0.12) (0.78) (0.21) (-0.61) (0.74) (-0.19) (0.23)

bp(Rmt - Rft) 0.692 1.240 1.093 0.929 1.137 1.260 1.135 1.189 0.985 1.011

(1.24) (2.35)** (2.21)** (2.29)** (2.45)** (13.98)*** (13.97)*** (14.53)*** (13.31)*** (9.68)***

sp(SMB) 1.140 0.678 0.934 1.333 1.534 0.801 0.906 0.650 0.818 0.961

(2.26)** (1.42) (2.08)** (3.63)*** (3.66)*** (7.80)*** (9.79)*** (6.97)*** (9.69)*** (8.07)***

hp(HML) -1.947 -1.635 -1.813 -1.115 -0.991 -0.323 0.038 0.150 0.322 -0.146

(-3.12)*** (-2.77)*** (-3.27)*** (-2.46)** (-1.91)* (-2.77)*** (0.37) (1.42) (3.37)*** (-1.08)

up(UMD) -1.392 -0.352 -0.578 -1.184 -1.250 -0.397 -0.320 -0.353 -0.345 -0.601

(-4.80)*** (-1.28) (-2.23)** (-5.60)*** (-5.17)*** (-5.92)*** (-5.30)*** (-5.80)*** (-6.26)*** (-7.74)***

Adj. R2 0.6847 0.6028 0.6830 0.7456 0.7232 0.7670 0.7555 0.7309 0.7284 0.6865

ap(Q5) – ap(Q1) 0.007 -0.001

t-statistics (0.43) (-0.20)

Note: The table presents time-series weighted least squares regression coefficients from the following regression, with weight being the square

root of the number of firms in each calendar month:

Rpt –Rƒt = ap + bp(Rmt – Rft) + spSMBt +hpHMLt + up(UMD)+ εpt,

Where Rpt is the equally weighted portfolio returns in calendar month t; Rƒt is the 30-day T-bill yield in month t; Rmt is the return on the value-

weighted CRSP index; SMBt is the return on small firms minus the return on large firms; HMLt is the return on high book-to-market stocks

minus the return on low book-to-market stocks in month t; and UMDt is the return on high prior return stocks minus the return on low prior

return stocks in month t. Quintiles are formed for each month by sorting all sample firms based on PFIBCADJ_AT. The t-statistic for ap(Q5) –

ap(Q1) in the table is from the intercept in an (unreported) equivalent regression of the difference between the conservative and aggressive

portfolio returns on the factor returns. T-statistics are reported using heteroskedasticity-corrected standard errors based on White (1980). *, **,

*** denote significance at the 10 percent, 5 percent, and 1 percent levels (for a two-sided test), respectively.

valuing ipos using pro forma earnings in the prospectus...

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