Proceedings of Paris Economics, Finance and Business Conference
13 - 15 April 2015, Crowne Plaza Hotel Republique, Paris, France, ISBN: 978-1-922069-73-3
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: jchen@hkbu.edu.hk
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).
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Proceedings of Paris Economics, Finance and Business Conference
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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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Proceedings of Paris Economics, Finance and Business Conference
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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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Proceedings of Paris Economics, Finance and Business Conference
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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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Proceedings of Paris Economics, Finance and Business Conference
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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 sheet 5 , pro forma condensed statements of income 6 , and accompanying explanatory notes. It is
ordinarily in columnar form showing condensed historical statements, pro forma adjustments 7, 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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Proceedings of Paris Economics, Finance and Business Conference
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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 postacquisition 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 threeyear 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 firstday 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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Proceedings of Paris Economics, Finance and Business Conference
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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
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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.
(INSERT TABLE 2 ABOUT HERE)
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.
(INSERT TABLE 3 ABOUT HERE)
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.
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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.
(INSERT TABLE 4 ABOUT HERE)
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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.
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(INSERT TABLE 5 ABOUT HERE)
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.
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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.
(INSERT TABLE 6 ABOUT HERE)
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
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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 postIPO 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).
(INSERT TABLE 7 ABOUT HERE)
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.
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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
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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
23
investors
in
forming
trading
strategies.
Proceedings of Paris Economics, Finance and Business Conference
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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)
Net revenues
Cost of net revenues
Gross profit
Operating expenses:
Research and development
Sales and marketing
General and administrative
Amortization of purchased intangible assets
Total operating expenses
Loss from operations
Interest income (expense), net
Loss on fair value of preferred stock warrant liabilities
Loss before income taxes
Provision for income taxes
Net loss
Accretion of redeemable convertible preferred stock
Net loss attributable to common stockholders
Net loss per share attributable to common stockholders—basic and diluted
Weighted average number of shares used to compute loss per share attributable
to common stockholders
Pro forma net loss per common share—basic and diluted
Weighted average number of shares used to compute pro forma net loss per
share—basic and diluted
Pro Forma
Adjustments
(unaudited)
—
7,897
(7,897)
Pro Forma
Combined
1,396
528
1,225
3,533
6,682
(14,579)
30
—
(14,549)
—
(14,549)
—
$(14,549)
24,179
8,322
5,344
3,533
41,378
(23,299)
672
(401)
(23,028)
(49)
(23,077)
(126)
(23,203)
(2.27)
4,325
(0.20)
5,898
10,223
(0.42)
35,886
18,941
54,827
Pro Forma
Adjustments
(unaudited)
—
500
(500)
Pro Forma
Combined
605
193
504
(21,400)
367
(19,731)
19,231
30,248
9,322
7,723
—
1,705
48,998
(12,032)
Entropic
RF Magic
Notes
41,471
31,099
10,372
$26,183
10,579
15,604
(a)(c)
11,601
4,112
2,192
—
17,905
(7,533)
883
(401)
(7,051)
—
(7,051)
(126)
(7,177)
(1.66)
11,182
3,682
1,927
—
16,791
(1,187)
(241)
—
(1,428)
(49)
(1,477)
—
$(1,477)
(c)
(c)
(c)
(b)
(e)
67,654
49,575
18,079
Entropic Communications, Inc.
Unaudited Pro Forma Condensed Consolidated Combined Statement of Operations
Nine Months Ended September 30, 2007
(in thousands, except per share data)
Net revenues
Cost of net revenues
Gross profit
Operating expenses:
Research and development
Sales and marketing
General and administrative
Acquired in-process research and development
Amortization of purchased intangible assets
Total operating expenses
Loss from operations
26
Entropic
RF Magic
Notes
82,377
54,491
27,886
15,204
5,444
9,580
(a)(c)
22,812
6,642
5,104
21,400
1,338
57,296
(29,410)
6,831
2,487
2,115
—
—
11,433
(1,853)
(c)
(c)
(c)
(d)
(b)
97,401
60,435
36,966
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Interest income (expense), net
Loss on fair value of preferred stock warrant liabilities
Loss before income taxes
Provision for income taxes
Net loss
Accretion of redeemable convertible preferred stock
Net loss attributable to common stockholders
Net loss per share attributable to common stockholders—basic and diluted
Weighted average number of shares used to compute loss per share attributable to
common stockholders
Pro forma net loss per common share—basic and diluted
Weighted average number of shares used to compute pro forma net loss per
share—basic and diluted
(64)
(2,006)
(31,480)
—
(31,480)
(95)
(31,575)
(3.89)
(389)
—
(2,242)
(30)
(2,272)
—
$(2,272)
(e)
89
—
19,320
19,320
—
$19,320
(364)
(2,006)
(14,402)
(30)
(14,432)
(95)
(14,527)
(1.21)
8,113
(0.71)
3,910
12,023
(0.26)
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.
27
Proceedings of Paris Economics, Finance and Business Conference
13 - 15 April 2015, Crowne Plaza Hotel Republique, Paris, France, ISBN: 978-1-922069-73-3
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)
Current assets
Cash
Accounts receivable, net
Prepaid expenses and other current assets
Deferred tax assets
Income taxes receivables
Total current assets
Property and equipment, net of accumulated depreciation and amortization
Other assets
Goodwill (1)
Intangibles, net of accumulated amortization
Debt issuance costs, net (5)
Deferred tax assets
Total other assets
Total assets
Current liabilities
Accounts payable
Accrued expenses (1)
Current maturities of long-term debt (3)
Deferred revenue
Total current liabilities
Preferred stock dividends, undeclared subject to payment on conversion to
common stock
Long-term debt, less current maturities (3)
Total liabilities
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
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
Preferred stock dividends, undeclared subject to payment on
conversion to common stock
Additional paid-in capital (4)
Retained earnings (4)
Total Stockholder’s equity (4)
Total liability and stockholders’ equity
28
Pro Forma
As Adjusted
Actual
Adjustments
850
63,114
9,195
4,059
227
77,445
3,184
—
—
—
—
—
—
63,114
9,195
4,059
227
77,445
3,184
48,216
15,059
1,006
1,010
65,291
145,920
10,810
—(556)
—10,254
10,254
59,026
15,059
450
1,010
75,545
156,174
3,381
28,859
10,139
2,033
44,412
—85
(2,286)
—(2,201)
3,381
28,944
7,853
2,033
42,211
11,506
54,275
110,193
(11,506)
(18,340)
(32,047)
—
35,935
78,146
1
9
10
37,750
(37,750)
-—
(11,506)
1,569
7,913
35,727
145,920
11,506
76,449
(7,913)
42,301
10,254
—78,018
—78,028
156,174
—
850
Proceedings of Paris Economics, Finance and Business Conference
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Addus HomeCare Corporation
Unaudited Pro Forma Consolidated Statements of Income
for the Calendar Year Ended December 31, 2008
(in thousands, except share and per share data)
Net service revenues
Cost of service revenues
Gross profit
General and administrative expenses (1)(2)
Depreciation and amortization
Total operating expenses
Operating income
Interest expense (5)
Interest and other income
Income from operations before taxes
Income tax expense (6)
Net income
Less: preferred stock dividends, undeclared subject to payment upon
conversion
Net income (loss) attributable to common shareholders
Net income per share of common stock:
Basic
Diluted
Weighted average number of shares outstanding: (7)
Basic
Diluted
Actual
236,306
167,254
69,052
52,112
6,092
58,204
10,848
(5,806)
51
5,093
1,070
4,023
Adjustments
—
—
—
(350)
—
(350)
350
2,232
—
2,582
989
1,593
Pro Forma
As Adjusted
236,306
167,254
69,052
51,762
6,092
57,854
11,198
(3,574)
51
7,615
2,059
5,616
(4,270)
(247)
4,270
5,863
—
5,616
(0.24)
(0.24)
0.54
0.53
1,019,250
1,019,250
10,496,251
10,528,979
Addus HomeCare Corporation
Unaudited Pro Forma Consolidated Statements of Income
for the Six Months Ended June 30, 2009
(in thousands, except share and per share data)
Net service revenues
Cost of service revenues
Gross profit
General and administrative expenses (1)(2)
Depreciation and amortization
Total operating expenses
Operating income
Interest expense (5)
Interest and other income
Income from operations before taxes
Income tax expense (6)
Net income
Less: preferred stock dividends, undeclared subject to payment upon
conversion
Net income (loss) attributable to common shareholders
Net income per share of common stock:
Basic
Diluted
Weighted average number of shares outstanding: (7)
Basic
Diluted
29
Actual
126,805
89,440
37,365
27,983
2,444
30,427
6,938
(2,180)
12
4,770
1,474
3,296
Adjustments
—
—
—
(176)
—
(176)
176
881
—
1,057
405
652
Pro Forma
As Adjusted
126,805
89,440
37,365
27,807
2,444
30,251
7,114
(1,299)
12
5,827
1,879
3,948
(2,284)
1,012
2,284
2,936
—
3,948
0.99
0.63
0.38
0.37
1,019,250
5,203,203
10,496,251
10,529,341
Proceedings of Paris Economics, Finance and Business Conference
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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.
30
Proceedings of Paris Economics, Finance and Business Conference
13 - 15 April 2015, Crowne Plaza Hotel Republique, Paris, France, ISBN: 978-1-922069-73-3
Table 1
Sample selection process
Total number of U.S. Domestic IPOs between 1997 and 2009 available from the Securities
Data Corporation (SDC)
Less:
Obs with missing CUSIPS
Obs with multiple entries
IPOs with non Form S-1 form registers
Financial services, insurance, and real estate firms
No matching firms in CRSP database
Listed date larger than 10 days
Non-ordinary stock
IPOs with offer price less than $5
S-1 IPOs by US non-financial firms listed on NYSE, NASDAQ, or AMEX
Less:
No whole-year financial statement data in prospectus
IPO sample for the period 1997 to 2009
Less:
No pro forma financial statements in prospectus
Pro forma earnings adjustments related to ―below the bottom line‖ items
Pro forma earnings adjustments without reporting specific adjustments used to calculate
the pro forma number
Final sample of pro forma IPOs before restrictions on firm-specific data
3,673
(40)
(12)
(1,137)
(355)
(8)
(10)
(107)
(5)
1,999
(87)
(334)
(756)
(22)
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.
31
Proceedings of Paris Economics, Finance and Business Conference
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Table 2
Distribution of pro forma and total IPOs by year and industry
Panel A: Distribution by year
Issuing Year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Number of pro forma IPOs
172
107
134
108
29
21
23
46
59
48
31
7
15
800
Percent (%)
21.5
13.4
16.8
13.5
3.6
2.6
2.9
5.8
7.4
6.0
3.9
0.9
1.9
100
Number of total IPOs
317
195
393
306
58
51
47
134
118
116
121
17
39
1,912
Percent (%)
16.6
10.2
20.6
16.0
3.0
2.7
2.5
7.0
6.2
6.1
6.3
0.9
2.0
100
Panel B: Distribution by SIC code
SIC code
1-1999
2000-2999
3000-3999
4000-4999
5000-5999
7000-7999
8000-8999
9000-9999
Number of pro forma IPOs
55
86
148
97
94
250
70
0
800
Percent
(%)
6.9
10.8
18.5
12.1
11.8
31.3
8.8
0.0
100
Number of total IPOs
68
236
460
183
169
637
158
1
1,912
Percent
(%)
3.6
12.3
24.1
9.6
8.8
33.3
8.3
0.1
100
Panel C: Distribution by Internet segment
Industry Segment
Internet
Non-Internet
Frequency
138
662
800
Percent (%)
17.3
82.8
100
Frequency
442
1,470
1,912
Percent (%)
23.1
76.9
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
32
Proceedings of Paris Economics, Finance and Business Conference
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repair, motion pictures, amusement and recreation services; SIC codes 8000-8999 = Service Industries: health,
legal, educational, social, museums, engineering, accounting, management, etc.
33
Proceedings of Paris Economics, Finance and Business Conference
13 - 15 April 2015, Crowne Plaza Hotel Republique, Paris, France, ISBN: 978-1-922069-73-3
Table 3
Classification of pro forma IPOs by adjustment category
Total IPOs
Adjustment Category
Frequency
COMBINATION
486
OFFER
284
TAX
138
RECAP
94
REORG
42
DISPOSE
29
STAND
20
ROLLUP
17
OTHER
16
Total adjustment
1,126
Total pro forma IPOs
800
Average adjustments per pro
1.41
forma IPO
Internet IPOs
Percent
(%)
43.2
25.2
12.3
8.3
3.7
2.6
1.8
1.5
1.4
100
Frequency
115
17
11
3
3
4
0
0
1
154
138
1.12
Percent
(%)
74.7
11.0
7.1
1.9
1.9
2.6
0.0
0.0
0.6
100
Non-Internet IPOs
Percent
Frequency
(%)
371
38.2
267
27.5
127
13.1
91
9.4
39
4.0
25
2.6
20
2.1
17
1.7
15
1.5
972
100
662
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.
34
Proceedings of Paris Economics, Finance and Business Conference
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Table 4
Descriptive Statistics of Selected Variables
Earnings Variables
PFIBC_AT
PFIBC_PS
IBC_AT
IBC_PS
Dependent Variables
FIBC_AT
OFFER
PRC
BHRET
Control Variables
CEQ_PS
RENTENTION
HIGHUW
VC
UPDATE
BHMKTRET
LOGMV
LOGBTM
UNDERPRICING
LOGAGE
TACC_AT
Internet IPOs
N Mean
Median
Std. Dev.
Non-Internet IPOs
N Mean
Median
Std. Dev.
138
138
138
138
-6.462
-0.669
-0.779
-0.369
-0.851
-0.473
-0.526
-0.226
44.293
1.238
1.390
0.718
662
662
662
662
-0.075
0.065
-0.040
0.110
0.036
0.189
0.022
0.137
0.857
2.043
0.422
2.304
114
138
138
138
-0.479
14.616
26.834
-0.561
-0.333
14.000
19.625
-0.945
0.669
4.984
19.887
0.933
613
662
662
662
-0.032
14.336
16.836
-0.018
0.020
14.000
15.408
-0.316
0.182
4.671
7.922
1.148
138
138
138
138
138
138
138
138
138
138
138
0.314
0.778
0.848
0.674
0.210
-0.140
6.359
0.005
0.711
1.815
-0.319
-0.061
0.824
1.000
1.000
0.174
-0.187
6.351
-0.002
0.397
1.609
-0.223
2.519
0.182
0.360
0.470
0.365
0.216
1.107
0.102
0.819
0.704
0.442
662
662
662
662
662
662
662
662
662
662
662
2.653
0.681
0.761
0.251
-0.015
0.184
5.752
0.099
0.156
2.769
-0.082
0.625
0.729
1.000
0.000
0.000
0.168
5.717
0.043
0.089
2.708
-0.063
10.625
0.215
0.427
0.434
0.204
0.343
1.178
0.285
0.279
1.043
0.233
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
35
Proceedings of Paris Economics, Finance and Business Conference
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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.
36
Proceedings of Paris Economics, Finance and Business Conference
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Table 5
Predictive Ability Tests
Variable
Intercept
IBC_AT
PFIBC_AT
Internet IPOs
(1)
(2)
0.290
0.349
(1.14)
(1.40)
0.092
(1.55)
0.002
(1.17)
PFIBCADJ_AT
Industry & Year Included
Dummies
No of Obs.
114
Adj. R2
0.1320
Non-Internet IPOs
(4)
(5)
0.057
0.059
(2.46)**
(2.54)**
0.265
(8.17)***
0.253
(12.99)***
(3)
0.289
(1.14)
0.090
(1.59)
(6)
0.059
(2.57)**
0.287
(11.86)***
Included
0.001
(0.68)
Included
Included
Included
0.223
(5.20)***
Included
114
0.1075
114
0.1242
613
0.3553
613
0.4087
613
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.
37
Proceedings of Paris Economics, Finance and Business Conference
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Table 6
IPO Pricing Tests
Panel A: Regression for OFFER
Variable
Intercept
POSIBC_PS
NEGIBC_PS
POSPFIBC_PS
NEGPFIBC_PS
Internet IPOs
(1)
(2)
-1.221
-2.163
(-0.37)
(-0.62)
0.033
(0.02)
-0.364
(-0.41)
-0.137
(-0.11)
-0.386
(-1.61)
PFIBCADJ_PS
CEQ_PS
RETENTION
HIGHUW
VC
Industry &
Dummies
No of Obs.
Adj. R2
0.272
(1.57)
8.322
(3.13)***
4.608
(5.06)***
-2.673
(-2.19)**
Year Included
114
0.1873
(3)
-1.850
(-0.56)
0.180
(0.12)
-0.554
(-0.60)
Non-Internet IPOs
(4)
(5)
8.443
8.274
(8.04)***
(8.21)***
1.023
(3.92)***
0.083
(0.79)
1.536
(5.84)***
0.025
(0.21)
(6)
8.475
(8.15)***
1.026
(4.00)***
0.095
(0.84)
0.343
(2.19)**
9.294
(3.43)***
4.497
(5.01)***
-2.503
(-2.03)**
Included
-0.588
(-1.52)
0.301
(1.75)*
9.034
(3.42)***
4.516
(5.02)***
-2.503
(-1.97)*
Included
0.035
(1.15)
0.829
(0.90)
3.593
(10.01)***
-1.524
(-3.59)***
Included
0.021
(0.81)
1.549
(1.88)*
3.538
(9.99)***
-1.480
(-3.46)***
Included
0.872
(2.12)**
0.037
(1.26)
1.434
(1.75)*
3.551
(9.93)***
-1.496
(-3.53)***
Included
114
0.1845
114
0.1854
613
0.2415
613
0.2605
613
0.2521
Note: See Table 4 for variable definitions. All continuous variables are windorized at 1 and 99% levels. Tstatistics 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
Proceedings of Paris Economics, Finance and Business Conference
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Table 6
IPO Pricing Tests
Panel B: Regression for PRC
Variable
Intercept
POSIBC_PS
NEGIBC_PS
POSPFIBC_PS
NEGPFIBC_PS
Internet IPOs
(1)
(2)
10.862
12.735
(1.44)
(1.55)
6.979
(1.61)
-0.040
(-0.02)
6.445
(1.63)
0.315
(0.26)
PFIBCADJ_PS
CEQ_PS
-0.040
(-0.09)
RETENTION
11.735
(1.91)*
HIGHUW
5.333
(2.07)**
VC
-3.830
(-1.03)
UPDATE
37.664
(10.24)***
Industry & Year Included
Dummies
No of Obs.
114
Adj. R2
0.5631
(3)
14.013
(1.72)*
6.489
(1.55)
0.674
(0.26)
Non-Internet IPOs
(4)
(5)
9.930
9.959
(8.75)***
(8.91)***
0.503
(2.39)**
0.109
(1.04)
0.813
(2.84)***
0.134
(1.25)
(6)
9.940
(8.80)***
0.704
(2.87)***
0.148
(1.39)
-0.022
(-0.05)
9.635
(1.37)
5.451
(2.11)**
-3.832
(-1.01)
37.954
(10.48)***
Included
2.312
(1.25)
-0.165
(-0.37)
8.734
(1.26)
5.518
(2.14)**
-4.429
(-1.20)
38.337
(10.38)***
Included
0.062
(2.04)**
1.458
(1.61)
3.909
(9.38)***
0.333
(0.61)
24.490
(17.58)***
Included
0.067
(2.25)**
1.351
(1.48)
3.886
(9.31)***
0.358
(0.65)
24.436
(17.39)***
Included
0.614
(1.81)*
0.070
(2.30)**
1.367
(1.51)
3.863
(9.29)***
0.390
(0.71)
24.399
(17.48)***
Included
114
0.5635
114
0.5649
613
0.5699
613
0.5710
613
0.5716
Note: See Table 4 for variable definitions. All continuous variables are windorized at 1 and 99% levels. Tstatistics 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
Proceedings of Paris Economics, Finance and Business Conference
13 - 15 April 2015, Crowne Plaza Hotel Republique, Paris, France, ISBN: 978-1-922069-73-3
Table 7
Future Return Tests: Event Time Regression
Variable
Internet IPOs
0.089
(0.08)
0.003
(1.07)
1.452
(1.46)
0.015
(0.14)
-0.168
(-0.28)
0.024
(0.17)
-0.115
(-1.17)
0.107
(0.99)
0.289
(1.97)*
0.027
(0.25)
Included
114
0.2833
Intercept
PFIBCADJ_AT
BHMKTRET
LOGMV
LOGBTM
TACC_AT
UNDERPRICING
LOGAGE
HIGHUW
VC
Industry & Year Dummies
No of Obs.
Adj. R2
Non-Internet IPOs
-0.301
(-0.91)
-0.053
(-0.22)
1.177
(3.74)***
0.018
(0.43)
0.318
(2.20)**
0.017
(0.09)
-0.264
(-1.92)*
0.018
(0.45)
0.096
(0.94)
0.096
(0.91)
Included
613
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.
40
Proceedings of Paris Economics, Finance and Business Conference
13 - 15 April 2015, Crowne Plaza Hotel Republique, Paris, France, ISBN: 978-1-922069-73-3
Table 8
Future Return Tests: Calendar Time Portfolio Regression
Variable
ap(Intercept)
bp(Rmt - Rft)
sp(SMB)
hp(HML)
up(UMD)
Adj. R2
ap(Q5) – ap(Q1)
t-statistics
Internet IPOs
Q1
Q2
0.005
0.005
(0.19)
(0.23)
0.692
1.240
(1.24)
(2.35)**
1.140
0.678
(2.26)**
(1.42)
-1.947
-1.635
(-3.12)*** (-2.77)***
-1.392
-0.352
(-4.80)*** (-1.28)
0.6847
0.6028
Q3
0.012
(0.56)
1.093
(2.21)**
0.934
(2.08)**
-1.813
(-3.27)***
-0.578
(-2.23)**
0.6830
0.007
(0.43)
Q4
0.002
(0.12)
0.929
(2.29)**
1.333
(3.63)***
-1.115
(-2.46)**
-1.184
(-5.60)***
0.7456
Q5
0.016
(0.78)
1.137
(2.45)**
1.534
(3.66)***
-0.991
(-1.91)*
-1.250
(-5.17)***
0.7232
Non-Internet IPOs
Q1
Q2
0.001
-0.002
(0.21)
(-0.61)
1.260
1.135
(13.98)*** (13.97)***
0.801
0.906
(7.80)***
(9.79)***
-0.323
0.038
(-2.77)*** (0.37)
-0.397
-0.320
(-5.92)*** (-5.30)***
0.7670
0.7555
Q3
0.003
(0.74)
1.189
(14.53)***
0.650
(6.97)***
0.150
(1.42)
-0.353
(-5.80)***
0.7309
-0.001
(-0.20)
Q4
-0.001
(-0.19)
0.985
(13.31)***
0.818
(9.69)***
0.322
(3.37)***
-0.345
(-6.26)***
0.7284
Q5
0.001
(0.23)
1.011
(9.68)***
0.961
(8.07)***
-0.146
(-1.08)
-0.601
(-7.74)***
0.6865
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 valueweighted 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.
41