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Contents lists available at ScienceDirect
The Quarterly Review of Economics and Finance
journal homepage: www.elsevier.com/locate/qref
Stock market bubble effects on mergers and acquisitions
David Y. Aharon a,b , Ilanit Gavious a,∗ , Rami Yosef a
a
b
Guilford Glazer Faculty of Business and Management, Ben-Gurion University, Israel
Ono Academic College, Israel
a r t i c l e
i n f o
Article history:
Received 18 November 2009
Received in revised form 3 May 2010
Accepted 20 May 2010
Available online xxx
JEL classification:
G1
G34
Keywords:
Technology bubble
Stock market bubble
Market cycles
M&A
Firm valuation
a b s t r a c t
We investigate if and how mergers and acquisitions are affected by trends in the capital market, and
particularly by a stock market bubble. Our main findings indicate that while the prevalence of M&A
increased during the technology bubble, the pricing of M&A did not change. Moreover, the bursting of
the bubble seems to have led to further cautiousness by investors, which extended throughout the years
subsequent to the bursting of the bubble, even when prices on the exchange had rebounded. While
we do not find robust evidence for changes in price multiples outside the exchange in concomitance
with the changes on the exchange, we document changes in the information used by investors to value
their targets. It seems that investors experienced a learning process in terms of the type of variables
preferred, appearing to be more cautious since the bubble burst. This learning process investors undergo
in concomitance to processes in the market seems to result in their being less affected by periodical or
cyclical sentiments of euphoria and depression in the capital market.
© 2010 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.
1. Introduction
The technology bubble on NASDAQ at the end of the 1990s led
to an unprecedented rise in stock prices of high-technology firms.1
Studies document a “Contagious Effect” in the capital market during the bubble with overpricing spilling over from the high-tech
sector towards more traditional sectors such as finance, manufacturing, trading and services (e.g., Brooks & Katsaris, 2005). In April
2000, the technology bubble burst, leading to a downturn in the
capital market, followed by a rebound starting in 2003. Hence, in
a span of only few years, the capital market underwent unusual
vicissitudes. While the affect of these vicissitudes on share prices
on the exchange is evident, extant research does not examine their
affect on transactions taking place outside the exchange. This study
attempts to fill this void in the literature. Using a broad sample of
4,166 U.S. public-firm targets acquired by other U.S. public firms
∗ Corresponding author at: Guilford Glazer Faculty of Business and Management,
Department of Business Administration, Ben-Gurion University, PO Box 653, BeerSheva 84105, Israel. Tel.: +972 8 6477538; fax: +972 8 6477691.
E-mail address: madaril@bgu.ac.il (I. Gavious).
1
See, e.g., Asness (2005), Ljungqvist and Wilhelm (2003), and Ofek and
Richardson (2002).
over the time period of 1993–2005, we conduct a comprehensive
analysis of transactions of mergers and acquisitions (henceforth,
“M&A”) from different aspects across four sub-periods surrounding the technology bubble. We thoroughly explore the prevalence
of M&A transactions throughout these sub-periods, their pricing –
as well as the factors affecting pricing (including the time factor),
and the information used by investors to value their targets.
We conduct our investigation by dividing the sample period
into the following time periods: the period prior to the technology bubble (“pre-bubble”: 1993 through 1997), the bubble period
(“bubble”: 1998 through March 2000), the bursting of the bubble
and the downturn in the capital market that followed the bursting
(“bursting of bubble”: April 2000 through 2002), and the rebound
that occurred in 2003 and continued throughout the end of our
sample period (“post-bursting”: 2003 through 2005). The sample
includes acquired firms from three major sectors: high-technology,
low-technology (manufacturing), and trading & services. Consistent with prior studies, financial institutions are excluded from
our sample to avoid the confounding effects of these highly regulated industries (e.g., Burgstahler & Eames, 2003; Core, Guay, &
Van Buskirk, 2003; De Franco, Gavious, Jin, & Richardson, 2008;
Rosner, 2003).
Notably, in contrast to investors buying shares on the exchange,
in large transactions outside the exchange, buyers conduct due dili-
1062-9769/$ – see front matter © 2010 The Board of Trustees of the University of Illinois. Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.qref.2010.05.002
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gence procedures as part of a comprehensive investigation of the
target firm’s financial information, to minimize the information
uncertainty faced by the buyer.2 We seek to explore whether and
how more informed buyers are affected by sentiments of euphoria
and depression in the capital market.
We document a considerable increase in the prevalence of M&A
transactions during the bubble for all sectors (a 95.0% increase in
the high-tech sector compared with 66.5 and 41.9% in low-tech
and trading & services, respectively), followed by a reduction to
pre-bubble levels at the bursting of the bubble. In the following
post-bursting sub-period, the frequency of M&A transactions in all
sectors kept declining, even at a higher pace than the reduction
during the bursting of the bubble, despite recovery in the capital
markets during that time.
We examine changes in the pricing of M&A transactions
throughout the four sub-periods, using a multiples analysis
approach. Employing both a univariate and multivariate analysis
that controls for differences in industry, time, firm size, profitability, risk and growth, we find that transaction multiples of high-tech
firms did not increase during the bubble, compared with their prebubble level. Nonetheless, there is some evidence of a decrease
in transaction multiples at the bursting of the bubble. No change
in multiples is documented between the bursting and the postbursting sub-periods. Similar results are documented for trading &
services firms. For low-tech firms, we find that transaction multiples did not vary at all across the four sub-periods. The findings
imply that investors buying shares outside the exchange were
not affected by the euphoric atmosphere on the exchange during
the bubble. The bursting of the bubble seems to have led to further cautiousness by these investors, which extended through the
years subsequent to the bubble bursting, even when prices on the
exchange rebounded.
While we do not find robust evidence for changes in valuation
multiples outside the exchange in concomitance with the changes
on the exchange, we document changes in the information used
by investors to value their targets across the four sub-periods.
The results reveal that the relations between financial information
and transaction values of equity have undergone unusual changes
throughout the investigated period. In particular, during the drastic events of the bubble and the bursting of the bubble, investor
valuations tended to rely on expectations for the future (proxied
by the change in sales and R&D expenditures) rather than on the
current performance of the target firm (proxied by current earnings). In contrast, at the sub-periods prior to and after these market
vicissitudes, current earnings rather than expectations for future
earnings are found to contribute to the explanation of the variation
in transaction prices, implying that investors may have been willing
to attach a higher price to a proven ability to generate higher earnings, but refrained from the risk of attaching a higher price based
on expectations for the future. We further compare the role that
accruals play as proxy for expectations about the target’s future
cash flows, versus the role of the cash flow component of earnings. Results indicate that during the bubble sub-period, investors
attached higher values to the accrual component of earnings. In
contrast, at the bursting of the bubble, accruals lost significance,
while cash flows became significant in the valuation of the targets. In the post-bursting sub-period, bidders seem to have placed
greater weight on operating cash flows than on accruals. Interestingly, given the high likelihood of accrual manipulation prior
2
De Franco et al. (2008) explain that “If this information uncertainty cannot be
eliminated at a minimal cost through additional information acquisition as part of
the due diligence process, then acquirers will require a higher rate of return”. This
higher required rate of return will reduce the target firm’s valuation.
to M&A transactions, the results imply that during the euphoric
bubble sub-period, investors outside the exchange did take more
risks, relying on possibly manipulated accruals and expectations
for future earnings. However, these investors seem to have undergone a learning process in terms of the type of variables preferred,
and in our case, appear to be more cautious and suspicious since
the bursting of the bubble with regards to the quality of earnings
reported prior to the transaction.
Our findings gain further support when we differentiate
between expected and unexpected accruals. Prior studies indicate
that the extent to which a firm manages its earnings would, in fact,
be expressed in the magnitude of the unexpected – discretionary –
accruals component of earnings (e.g., Dechow & Skinner, 2000). We
show that during the bubble, investors were affected by the noisy,
less persistent component of earnings, i.e., by unexpected accruals,
rather than by expected accruals and cash flows. Markedly, this
changed when the bubble burst. We find evidence that during the
bursting of the bubble, investors discounted the price they were
willing to pay once they detected upward earnings manipulation,
as expressed in positive unexpected accruals. Investors seem to
remain cautious with respect to indications for earnings manipulation through post-bursting years. Our results indicate that the
use investors make of accounting variables in valuations of target
firms is a dynamic process that changes over time. It seems that
this learning process investors undergo in concomitance with processes in the market results in their being less affected by periodical
or cyclical sentiments of euphoria and depression in the capital
market.
The remainder of the paper proceeds as follows. Section 2 contains our literature review. Section 3 describes our sample and
presents our hypotheses. Section 4 discusses our research methods
and results. Section 5 summarizes and concludes.
2. Literature review
2.1. The technology bubble
The technology bubble has been investigated thus far with
regards to different aspects. For example, Ljungqvist and Wilhelm
(2003) investigate the technology bubble effect on IPO pricing. They
find that the IPO pricing behavior during the “dot-com bubble” was
affected by firm characteristics such as marked changes in pre-IPO
ownership structure and insider selling behavior over the period.
When controlling for these elements, they find that differences
in IPO underpricing between the bubble period and the 3 years
preceding it are much reduced. Brunnermeier and Nagel (2004)
examine the response of hedge funds to the technology bubble.
They present findings that question the efficient markets notion
that rational speculators stabilize prices; i.e., their findings indicate that hedge funds did not exert a correcting force on stock
prices during the bubble. Specifically, they show that these funds
were heavily invested in technology stocks capturing the upturn,
however avoiding the downturn by reducing positions in stocks
that were about to decline. Brooks and Katsaris (2005) document
that the high-tech sector had a “Contagious Effect” on other sectors
during the bubble. Other studies investigated analyst failure to predict and warn investors of the bubble. This is mainly explained by
analyst tendency to over-optimistic forecasts (see, e.g., Liu & Song,
2001).
2.2. Mergers and acquisitions
The extent literature on mergers and acquisitions focuses on
several important aspects of these transactions. Many studies
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have investigated the various reasons for M&A to take place.
These include, inter alia, creation of synergies, economics of
scale and product scope (e.g., Brush, 1996; Healy, Palepu, &
Ruback, 1990; Ravenscraft & Scherer, 1987; Seth, 1990; Seth,
Song, & Pettit, 2000)3 ; risk diversification (e.g., Chatterjee &
Lubatkin, 1990; Markides, 1994; Nahavandi & Malekzadeh, 1988;
Ravenscraft & Scherer, 1987); obtaining market (monopolistic)
power (e.g., Borenstein, 1990; Chatterjee, 1991; Kim & Singal,
1993; Ravenscraft & Scherer, 1987); management change due to
poor performance (e.g., Harris & Robinson, 2002; Ravenscraft &
Scherer, 1987; Resti, 1998; Stavros, 1997; Vennet, 1996); maximizing managers utility and minimizing agent conflict (e.g., Datta,
Iskandar-Datta, & Raman, 2001; Hadlock, Houston, & Ryngaert,
1999; Kesner, Shapiro, & Sharma, 1994; North, 2001). Notably,
studies show that larger firms are more likely to experience agency
problems that lead to empire building and hubris in takeover bidding acquirers (Jensen, 1986; Loderer & Martin, 1990; Moeller,
Schlingemann, & Stultz, 2004; Roll, 1986; Schwert, 2000).
Some studies have compared acquisitions of private versus
public firms. These studies investigate the existence of a private
company discount. While it is widely asserted in the practitioner
literature that private firms are evaluated at a discount relative
to public firms, the empirical evidence presented to support the
existence of such a discount does not produce consistent inferences (Pratt, 2001). For example, Officer (2007) attributes much
of the private company discount to sellers who accept higher discounts because they need to liquidate their investments. Phillips
and Freeman (1995), on the other hand, show that after controlling
for size, profitability, and whether the transaction occurred in the
banking industry there is no significant discount.
De Franco et al. (2008) estimate the private company discount
to range from 21% to 37% using enterprise value to EBITDA and
enterprise value to sales acquisition multiples.4 Koeplin, Sarin, and
Shapiro (2000) estimate a discount of 20–30% for enterprise value
to EBIT and EBITDA multiples, but none for enterprise value to sales.
Officer (2007) estimates the private company discount to be in the
15–30% range based on price-earnings, enterprise value to EBITDA,
and enterprise value to sales multiples; however, he finds that the
price to book value multiple is not lower, but significantly higher,
for private firms.
Another strand of research explores the performance of M&A.
Most studies on long-run performance for mergers and acquisitions
document either negative or insignificant long-term abnormal
returns (see, for example, Conn, Cosh, Guest, & Hughes, 2005;
Elnathan, Gavious, & Hauser, 2009; Loughran & Vijh, 1997;
Sudarsanam & Mahate, 2003).5 Other studies show that in a bullish
stock market, firms with overpriced shares are more likely to initiate an M&A transaction, preferring share rather than cash payment
(see, e.g., Shleifer & Vishney, 2003). Forms of payment in M&A
transactions are also investigated in additional contexts. For exam-
3
Synergies are likely to be higher when the primary business of the acquirer and
the target are in the same industry, compared to when the industries are different
(Morck, Shleifer, & Vishny, 1990). De Franco et al. (2008) explain that “An acquirer
in the same industry as the target will more likely understand the target’s business model as well as its risks and opportunities. These acquirers experience less
information asymmetry and likely require less effort in the due diligence process”.
4
De Franco et al. (2008) suggest that the private company discount can be
explained by lower earnings quality in private firms. De Franco, Gavious, Jin, and
Richardson (2009) suggest another explanation for the private company discount
that is related to the information quality facing the buyer. Specifically, they present
evidence that (not) hiring a Big 4 auditor increases (decreases) the sale proceeds of
private firms.
5
Except for Elnathan et al. (2009), who document a −56% abnormal return three
years from the time of transaction, the lowest returns documented in other studies
are about −20% for the same period.
3
ple, the use of equity as a form of payment is associated with
greater information asymmetry between the bidder and the target
(Hansen, 1987). Additionally, acquirers using common shares to
purchase the target tend to create new equity block holders.6 The
creation of outside block holders increases acquirer firm value if
these block holders more effectively monitor management (Chang,
1998; Shleifer & Vishny, 1986). Notably, thus far, no study has conducted a thorough analysis of the affect of stock market trends –
and particularly bubbles – on M&A transactions.
3. Data and hypotheses
3.1. Data
Our sample of public transactions is taken from the Thomson
Financial SDC database of mergers and acquisitions. The sample
includes 4166 U.S. public-firm targets acquired by other U.S. public firms over the time period of 1993–2005. In the database,
the buyer generally holds controlling interest after the transaction. For the sake of sample homogeneity, we eliminated firms
in which the buyer does not hold controlling interest after the
transaction.7
While the SDC database includes select financial statement data,
it does not contain all the data we need for our tests. Hence, we
obtain all financial statement data from Compustat. Public firms
with insufficient Compustat data are excluded from the analysis. In
the database, we identify acquired firms from four main sectors:
high-technology, low-technology (manufacturing), trading & services and finance. Consistent with prior studies, financial firms are
excluded from our sample to avoid the confounding effects of these
highly regulated industries. Also, following previous literature (e.g.,
Brown, Lo, & Lys, 1999; Collins, Maydew, & Weiss, 1997; Core et al.,
2003; De Franco et al., 2008), we restrict our sample to firms with
positive book value of equity. To mitigate the effect of outliers in
our analyses, we winsorize the top and bottom 1% of continuous
variables. We winsorize outliers instead of deleting them to conserve data. The results do not change qualitatively when outliers
are deleted.
We focus our analysis on M&A transactions across four main
time periods surrounding the technology bubble: the pre-bubble
sub-period (1993 through 1997), the bubble (1998 through March
2000), the bursting of the bubble (April 2000 through 2002), and
the post-bursting sub-period (2003 through 2005). This categorization is supported by primary market measures (NASDAQ returns,
venture capital fund activities measures and generally accepted
economic indicators (e.g., GDP and private consumption))8 and is
6
The creation of new equity block holders in this context is especially prevalent in
acquisitions of private firms, because private firm ownership is highly concentrated.
7
Our focus on controlling interests also allows us to abstract from the issue of
minority-interest discounts.
8
NASDAQ cumulative returns for the 5-year period beginning in 1993 and ending in 1997 are about 134%; for the 2.25-year period of 1/1998–3/2000 they are
about 189%, for the 2.75-year period of 4/2000–12/2002, −67%, and for the following 3 years, 1/2003–12/2005, around 59%. VentureXpertTM Database by VE & NVCA
displays a gradual and relatively slow increase in venture capital (VC) fundraising
in US from 1993 through 1997. Between 1998 and 2000, the process accelerates,
with amounts raised increasing dramatically, reaching up to $106 billion in 2000.
In 2001 and 2002, the downturn in the capital market that followed the bursting
of the bubble led to fundraising crashing down as low as $3.8 billion in 2002. Then,
a rebound occurred in 2003 and continued, with fundraising reaching up to $28.6
billion in 2006. The number of US venture-backed IPOs as well as the amounts raised
in these IPOs present a similar pattern. The number of venture-backed mergers and
acquisitions (average deal value) increased consistently from 1998 [253 deals ($59
million)] to 2000 [458 deals ($214 million)]; steadily decreased in 2001–2002, from
402 deals with average deal value of $54 million in 2001 to 380 deals with average
deal value of $28 million in 2002. In 2003, 2004 and 2005, the number of mergers and
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consistent with prior studies (see, e.g., Brooks & Katsaris, 2005;
Brunnermeier & Nagel, 2004; Gavious & Schwartz, 2008).
Table 1 contains descriptive statistics for our sample, by time
categories. Panel A presents unscaled financial variables. The
total transaction values as well as stock market values increased
(decreased) significantly during the (bursting of) the bubble. In
the post-bursting sub-period, the total values generally remained
similar to those in the bursting sub-period. The accountingbased size measures – book value of equity and total assets –
are less affected by the trends in the market. Specifically, book
values and total assets increased significantly during the bubble, however remained unchanged during the subsequent two
sub-periods.9
Panel B of Table 1 shows scaled financial variables. Interestingly,
sales growth does not seem to have changed (or more specifically,
did not increase) during the bubble. Moreover, when the bubble
burst, sales growth significantly increased, on average. A decrease
in sales growth is apparent in post-bursting years rather than when
the bubble burst. Additionally, three earnings measures – profit
margin, ROA and ROE – indicate that firm’s profitability was lower
during the bubble than in pre-bubble years, and that it continued
to decline during the bursting of the bubble. In post-bursting years,
profit margin significantly increased, but not enough to increase the
return on equity. The data implies that the increase in the volume
of transactions during the bubble is not supported by indications
for better performance by these firms.
3.2. Hypotheses
This paper explores if, and to what extent, M&A transactions
were affected by the growth and bursting of the technology bubble.
We hypothesize that the frequency as well as the valuations of M&A
transactions dramatically increased during the bubble, particularly
within the high-tech sector, in concomitance with technology stock
pricing on the exchange rising to unprecedented levels. Then, following the bursting of the bubble, we expect to find that M&A
frequency and pricing dropped, however we do not conjecture a
hypothesis regarding the intensity of the decrease, as the extreme
decline in share prices may have created favorable opportunities for
bidders. For post-bursting years, we expect to find evidence for an
increase in M&A frequency and pricing, in concomitance with the
rebound in the capital markets. Again, the intensity of this recovery in M&A is not obvious, as investors in the post-bubble era are
expected to be more skeptical and cautious in their investment
decisions.
We also explore whether, and how, the relevance of financial
variables for explaining transaction prices changed throughout the
investigated period. Extent research investigates the time-series
properties of the value-relevance of accounting information for
firms generally (e.g., Brown et al., 1999; Francis & Schipper, 1999;
Lev & Zarowin, 1999). These studies show that the value-relevance
of financial information, measured by the association of stock
returns with earnings and book value of equity, has declined consistently over time. In addition, Core et al. (2003) find that the ability
of traditional financial variables to explain firm value of both high-
acquisitions (average deal value) reached 338 ($39 million), 407 ($57 million) and
356 ($77 million), respectively. Finally, we utilize generally accepted economic indicators to examine the robustness of our time period categorization. These indicators
include GDP of the Business Sector, private consumption, and private investments.
All these measures, in general, support the pattern described above, as the activity in the capital market is naturally related to and affected by the state of the
economy.
9
Scaled size measures throughout the four sub-periods are analyzed in Section
4.2.
tech and low-tech firms decreased in the “New Economy Period”
(1996–1999), leading them to conclude that there is a greater variation in firm values during the “New Economy Period” that remains
to be explained.10 While there is an extensive body of research on
the value-relevance of financial statement information in general,
and for high-technology industries in particular, no study thus far
has investigated if, or how, this relevance is affected by a stock market bubble and the fluctuations that occur in the capital market after
the bubble bursts. We predict that the market is adaptive in that,
in concomitance with the occurrence of major events, investors
experience a learning curve, which is reflected in changes in the
(importance of the) role accounting variables play in the valuation
of an acquired firm. We conjecture that the relevance of conservative accounting decreases (increases) in times of euphoria or an
upturn (crash or a downturn) in the market.
4. Research methods and results
4.1. Frequency of M&A transactions
The prevalence of M&A transactions throughout the sample
period is presented in Table 2. In panel A of Table 2, we present
the total number of transactions in each sub-period, by sector. To
account for the different length of each sub-period, we further scale
the total number of transactions executed during a sub-period by
the number of months in this sub-period. The estimated number
of monthly transactions, as well as the percentage change in the
number of monthly transactions from the preceding sub-period, is
displayed in panel B of Table 2. The results indicate that a considerable increase in the prevalence of monthly transactions occurred
during the bubble – for all sectors. As expected, the highest rate
of increase in the frequency of M&A transactions during the bubble occurred in the high-tech sector – 95.0% compared with 66.5%
and 41.9% in low-tech and trading & services, respectively. With
the bubble bursting, the frequency of monthly M&A transactions
scaled back, approximately to their pre-bubble level. In the following post-bursting sub-period, the prevalence of M&A transactions
in all sectors kept decreasing, even at a higher pace relative to the
reduction during the bursting of the bubble. This downward trend
in the prevalence of M&A during post-bursting years is surprising,
as in these years capital markets demonstrated recovery.
We further differentiate between transactions by size, where
size is defined as the value of 100% of the acquired company’s equity
based on the price paid by the acquirer. Analyzing the distribution
of our sample transactions by size, we find that 45% of the transactions were valuated in the range of $0–100 million, 40% in the
range of $100–1,000 million, and 15% were over $1,000 million. We
refer to these size categories as small, medium and large transactions, respectively. In Table 3, we present the frequency of monthly
transactions throughout the four sub-periods for each size category
separately, to account for a possible size effect on our results.11 The
results indicate that the trend of an increase during the bubble and
a decrease during the bursting of the bubble, followed by a contin-
10
The studies investigating changes in the relevance of accounting information
over time attribute their findings of a consistent reduction in value-relevance to the
growth and importance of intangibles in the economy that are either not booked or
are treated improperly by GAAP. A significant body of literature explores whether
financial accounting is suited for a changing economy, which increasingly relies on
science-based emerging industries. A major area of research examines the valuerelevance of accounting data for the case of high-tech industries. These studies yield
mixed results (see, e.g., Amir & Lev, 1996; Callen, Gavious, & Segal, 2009; Core et al.,
2003; Ely, Simko, & Thomas, 2003; Hirschey, Richardson, & Ruback, 2001; Rajgopal,
Shevlin, & Venkatachalam, 2003).
11
We conduct sensitivity analyses for value ranges included in our size categories. Inferences remain qualitatively similar to those presented for the $0–100,
$100–1,000, and over $1,000 million ranges.
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Table 1
Descriptive statistics.
Pre
Bubble
Bursting
Post
Panel A: Unscaled financial variables
No. of obs.
1,580
1,174
901
511
Transaction value
Mean
499.390
1,073.723
813.815
679.483
Median
105.550
188.775
114.321
127.261
Std. Dev.
1,380.665
2,530.984
2,139.435
1,736.954
Enterprise value
Mean
778.173
1,628.349
1316.408
881.244
Median
172.757
262.540
176.947
189.244
Std. Dev.
2,537.520
4,851.898
3,926.144
2,591.092
Market value
Mean
562.537
1,136.570
866.223
729.221
Median
130.776
202.559
128.569
133.914
Std. Dev.
1,497.021
2,648.847
2,245.783
1,830.244
Book value
Mean
162.047
279.494
254.074
264.927
Median
44.800
59.600
57.400
58.350
Std. Dev.
405.644
703.586
648.657
658.747
Total assets
Mean
446.887
861.405
818.246
731.485
Median
93.900
123.400
110.700
123.600
Std. Dev.
1,420.975
3,127.603
2,734.938
2,850.455
Panel B: Scaled financial variables
Sales growth%
Mean
0.313
0.332
0.489
0.114
Median
0.128
0.122
0.112
0.034
Std. Dev.
0.728
0.838
1.222
0.599
Profit margin
Mean
−0.015
−0.104
−0.295
−0.152
Median
0.097
0.095
0.054
0.079
Std. Dev.
0.822
1.024
1.213
1.083
ROA
Mean
0.073
0.042
−0.052
0.008
Median
0.116
0.107
0.069
0.077
Std. Dev.
0.215
0.262
0.352
0.262
ROE
Mean
0.170
0.114
−0.089
0.001
Median
0.240
0.217
0.143
0.144
Std. Dev.
0.787
0.774
1.081
0.943
Bubble- Pre
BurstingBubble
PostBursting
574.333
(0.000)
83.225
(0.000)
−259.908
(0.015)
−74.454
(0.000)
−134.332
(0.222)
12.940
(0.100)
850.175
(0.000)
89.783
(0.000)
−311.940
(0.009)
−85.593
(0.001)
−435.163
(0.034)
12.297
(0.994)
574.033
(0.000)
71.783
(0.000)
−270.346
(0.016)
−73.990
(0.000)
−137.002
(0.237)
5.345
(0.707)
117.447
(0.000)
14.800
(0.000)
−25.420
(0.421)
−2.200
(0.407)
10.852
(0.777)
0.950
(0.924)
414.518
(0.000)
29.500
(0.000)
−43.159
(0.754)
−12.700
(0.417)
−86.760
(0.595)
12.900
(0.699)
0.019
(0.576)
−0.006
(0.316)
0.157
(0.002)
−0.010
(0.829)
−0.375
(0.000)
−0.078
(0.000)
−0.089
(0.022)
−0.002
(0.279)
−0.191
(0.000)
−0.041
(0.000)
0.143
(0.033)
0.025
(0.003)
−0.031
(0.002)
−0.009
(0.030)
−0.094
(0.000)
−0.038
(0.000)
0.060
(0.001)
0.008
(0.226)
−0.056
(0.082)
−0.023
(0.007)
−0.203
(0.000)
−0.074
(0.000)
0.090
(0.124)
0.001
(0.275)
This table reports descriptive statistics for our sample of 4,166 target firms. Extreme values (top and bottom 1%) of continuous variables are winsorized. P-Values for
differences between the means and the medians of each variable across the four sub-periods are presented in parentheses (two-sided tests). Transaction value is the sale
price of firm’s equity. Enterprise value is the sale price of firm’s equity plus total liabilities less current liabilities. Market value is market value of common shares outstanding
measured based on target stock price 1 week prior to the original announcement of the transaction. Book value of equity and Total assets are from the target firm’s most
recent annual fiscal period ending prior to the date of the sale transaction. Sales growth% is the percentage change in the annual sales. Profit margin is EBITDA divided by Sales.
EBITDA is earnings before interest, taxes and depreciation and amortization. ROA is EBITDA divided by Total assets. ROE is income before extraordinary items divided by Book
value. All financial statement data is for the target firm’s most recent annual fiscal period ending prior to the date of the sale transaction and are measured in $millions.
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Table 2
Frequency of transactions by sector and time period.
Panel A: Total number of transactions throughout the period
Sector
Bursting
Post
High-Tech
Low-Tech
Trading & Services
Total
1,444
1,078
1,644
Pre
489
435
656
Bubble
429
326
419
324
212
365
202
105
204
Total
4,166
1,580
1,174
901
511
Panel B: Monthly transactions throughout the period
Sector
Total
Pre
Bubble
# of trans.
# of trans.
# of trans.
% change
Bursting
# of trans.
% change
Post
High-Tech
Low-Tech
Trading & Services
10.028
7.486
11.417
8.150
7.250
10.933
15.889
12.074
15.519
0.950
0.665
0.419
9.818
6.424
11.061
−0.382
−0.468
−0.287
5.611
2.917
5.667
−0.428
−0.546
−0.488
Total
28.931
26.333
43.482
0.651
27.303
−0.372
14.195
−0.480
# of trans.
% change
Panel A of this table presents the total number of M&A transactions executed in each of our sample sub-periods, by sector. In panel B, the total number of M&A transactions
in each sub-period, and in each sector, is scaled by the number of months in the respective sub-period. The sub-periods are categorized as follows: 1/1993–12/1997 “Pre”
bubble; 1/1998–3/2000 “Bubble”; 4/2000–12/2002 “Bursting” of bubble; and 1/2003–12/2005 “Post” bursting. % change in panel B represents the percentage change in the
number of monthly transactions from the preceding sub-period.
Table 3
Frequency of monthly transactions by sector and time period when transaction size is differentiated.
Sector
Pre
Bubble
# of trans.
# of trans.
Panel A: Small-size transactions
High-Tech
3.133
Low-Tech
2.950
Trading & Services
4.100
Bursting
% change
# of trans.
Post
% change
# of trans.
% change
5.630
3.444
4.852
0.797
0.167
0.183
4.424
1.818
4.061
−0.214
−0.472
−0.163
2.221
0.922
2.283
−0.498
−0.493
−0.438
10.183
13.926
0.368
10.303
−0.260
5.426
−0.473
Panel B: Medium-size transactions
High-Tech
3.083
Low-Tech
2.433
Trading & Services
4.050
5.852
5.259
6.259
0.898
1.162
0.545
3.394
2.455
3.727
−0.420
−0.533
−0.405
2.139
1.167
1.556
−0.370
−0.525
−0.583
9.567
17.370
0.816
9.576
−0.449
4.861
−0.492
Panel C: Large-size transactions
High-Tech
0.933
Low-Tech
0.850
Trading & Services
1.050
3.370
2.444
2.630
2.612
1.875
1.505
1.515
1.758
1.030
−0.550
−0.281
−0.608
1.000
0.472
0.944
−0.340
−0.732
−0.083
Total
8.444
1.981
4.303
−0.490
2.417
−0.438
Total
Total
2.833
This table presents the number of M&A transactions in each sub-period, and in each sector, scaled by the number of months in the respective sub-period, for different
categories of size of transaction. In panels A, B and C the frequency of monthly transactions is presented for small, medium and large transactions, respectively, where small
transactions fall in the range of $0–100 million, medium fall in the range of $100–1,000 million, and large transactions are over $1,000 million paid by the acquirer for 100% of
the acquired company’s equity. The sub-periods are categorized as follows: 1/1993–12/1997 “Pre” bubble; 1/1998–3/2000 “Bubble”; 4/2000–12/2002 “Bursting” of bubble;
and 1/2003–12/2005 “Post” bursting. % change represents the percentage change in the number of monthly transactions from the preceding sub-period.
uing reduction in the post-bursting years, is robust to transaction
size.
4.2. Analysis of transaction multiples
We now move to analyze changes in the pricing of M&A transactions throughout the four sub-periods. We test whether the price
paid per dollar of accounting fundamental has changed in concomitance with the trends in the capital market. We utilize the
Price-Earnings (P/E) multiple, which is most commonly used in
practice to value firms, and which has received growing academic
attention in the past decade.12 P is sale price of the firms’ equity and
12
See for example, Alford (1992), Bhojraj and Lee (2002), Cheng and McNamara
(2000), Lie and Lie (2002), Liu, Nissim, and Thomas (2002), Mukherjee, Kiymaz, and
Baker (2004), and Penman (1996).
E is net income before extraordinary items. We also employ in our
analyses additional multiples widely used to value firms, Price-toBook (P/B) and Enterprise Value-to-Sales (EV/S).13 B is book value of
equity. EV is defined as the sale price of the firms’ equity plus total
liabilities less current liabilities and hence it measures the value of
the entire enterprise as opposed to just the equity value. S is the
firm’s total revenues. E, B and S are from the most recent fiscal year
ending prior to the date of the sale transaction.
Given that multiples are restricted to positive values, the P/B
and the EV/S multiples have an important advantage as they can
be used for firms with negative earnings, which leads to a larger
sample that better represents the population of firms (see, e.g.,
13
See, e.g., Kaplan and Ruback (1995), Bhojraj and Lee (2002), Lie and Lie (2002),
Liu et al. (2002), and Mukherjee et al. (2004).
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Bhojraj & Lee, 2002; De Franco et al., 2008). Notably, the prevalence of negative earnings in high-technology firms is relatively
high due to the immediate expensing of research and development
(R&D) expenses in accordance with Generally Accepted Accounting Principles (see for example, Amir & Lev, 1996; Ely et al.,
2003; Hand, 2005; Lev & Sougiannis, 1996; Shortridge, 2004).14
Our sample is restricted to firms with positive book value of
equity. In practice, however, book values may be negative, and
thus the EV/SALES multiple is often useful for evaluation of these
firms.
Table 4 presents the univariate differences in transaction multiples across the four sub-periods. In panel A of Table 4, the results
are presented for the full sample, and in panels B–D, we report
results for the high-tech, low-tech and trading & services subsamples. The results for the full sample imply that, as expected,
transaction multiples increased during the bubble, decreased when
the bubble burst and increased in the post-bursting sub-period.
These differences are generally statistically significant at the 5%
level. Nevertheless, substantial inferences from a multiples analysis
should be drawn when focusing on a specific industry, as industries differ in their characteristic financial ratios. Indeed, inferences
change when we turn to focus on each industrial sector separately.
Focusing initially on the high-tech sector, we find inconsistent
results for the three multiples. While transaction P/E multiples
seem to not have changed significantly throughout the entire
sample period, the P/B multiple indicates a significant increase
during the bubble followed by a significant decrease at the bursting of the bubble. In the post-bursting sub-period, P/B multiples
did not change significantly from their level during the bursting
sub-period. The results for the EV/S ratio are not robust (the parametric tests do not support results of non-parametric tests and
vice versa). In all, results regarding changes in transaction price
multiples of high-tech firms across the sample sub-periods are
not robust. In other words, we do not find consistent evidence
to support our expectation of an increase in pricing of high-tech
firms in transactions taking place during the bubble, nor for a
decrease during the bursting of the bubble. Interestingly, similar inferences are obtained when focusing on the low-tech sector;
with the exception of evidence for a reduction in P/B in the postbursting sub-period, both parametric and non-parametric tests
imply that multiples did not change significantly throughout the
sample period. For the trading & services sector, results imply that
transaction multiples generally increased during the bubble and
significantly decreased during the bursting of the bubble. Surprisingly, the technology bubble seems to have affected transaction
prices of non-technology-based firms more than it did technologybased firms.
To further investigate the differences in valuation multiples
throughout the four sub-periods, we move from a univariate analysis to a multivariate analysis. Differences in multiples can derive
from factors other than industrial affiliation, time period or market trend. Other factors affecting valuation multiples include the
target firm’s profitability, risk and growth. For example, higher
risk implies that the buyer, given a proxy for expected future cash
flows, would use a higher discount rate, resulting in lower firm
valuations.
The multivariate model that we employ is (see also, e.g., Bhojraj
& Lee, 2002; De Franco et al., 2008; Francis, LaFond, Olsson, &
14
We also apply another multiple, Enterprise Value to EBITDA which, like P/E and
in contrast to EV/S, cannot be used when the accounting fundamental is negative.
Nonetheless, this multiple yields the same qualitative inferences (untabulated).
7
Schipper, 2005):
ValuationRatio = ˇ0 +1 ROE + 2 PM + 3 SalesGrowth + 4 Size
+ 5 Lev + 6 PreBubble + 7 Burst
+ 8 PostBurst + ε
(1)
This equation is estimated separately for the P/E, P/B and EV/S
ratios. Consistent with prior studies, we use E/P, B/P and S/EV (the
inverse of the P/E, P/B and EV/S multiples, respectively) as the
dependent variables. Beatty, Riffe, and Thompson (1999) show that
applying the inverse of multiples when using the method of comparables is advantageous, since the accounting variable is considered
to be a noisy measure for expected cash flows, and thus placing it
in the denominator leads to estimated coefficients that are positively biased. However, placing it in the numerator yields unbiased
estimated coefficients.
The control variables are defined as follows: ROE is net income
before extraordinary items divided by the book value of equity;
PM (profit margin) is EBITDA divided by Sales; SalesGrowth is the
percentage change in annual sales; Size is the log of total assets;
Leverage is the ratio of total liabilities less current liabilities to
total assets. PreBubble (Burst,PostBurst) is an indicator variable that
equals 1 if the date of the transaction falls within the pre-bubble
(bursting, post-bursting) sub-period, 0 otherwise. This multivariate analysis is conducted for each industry separately to control for
industry effects. Size and sales growth serve as proxies for risk and
growth, respectively. We also take into account the role of profitability – ROE and profit margin – for our multiples. Consistent
with prior studies (e.g. Bhojraj and Lee), we exclude ROE from the
E/P regression because of the mechanical relation between these
two variables.15
Theory suggests that growth and profitability should be positively correlated with multiples. Hence, we expect that our proxies
of growth and profitability should be negatively related to the
inverse of multiples. We do not form a prediction as to the sign
of the coefficient on leverage and size. As for financial leverage,
while on the one hand it captures risk (and hence should be negatively related to price multiples), on the other hand it serves as
a proxy for creditors’ demand for high quality and conservative
earnings16 (and hence should be positively related to price multiples). Size is another proxy for risk. For example, smaller firms may
have lower multiples, and thus larger inverse of multiples, consistent with smaller firms being riskier than larger firms (see Francis
et al., 2005). On the other hand, size may capture value drivers
beyond firm risk, such as the future growth opportunities (e.g.,
De Franco et al., 2008), and hence the relation between size and
transaction multiples is equivocal. The coefficients on PreBubble,
Burst, and PostBurst capture the mean difference in the respective
multiple between each of these sub-periods and the bubble subperiod, after controlling for differences in industry composition,
risk, profitability and growth.
The results of the multivariate analysis are presented in Table 5
. Panel A (B and C) of Table 5 displays the results for high-tech
(low-tech and trading & services, respectively) industries. For the
high-tech sector, we find that ROE, PM and SalesGrowth are, as
expected, significantly negatively related to the inverted transaction multiples. The coefficient on size is significantly negative for
all three multiples, whereas the coefficient on leverage is signifi-
15
ROE and P/E are both defined as the ratio of net income before extraordinary
items to equity value. For ROE, equity value is taken at book value, whereas for P/E
it is the transaction value of the firms’ equity.
16
E.g., Fama (1985), Berlin and Loeys (1988), and De Franco et al. (2008).
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Table 4
Univariate analysis of transaction multiples by sector and time period.
Pre
Bubble
Bursting
Post
Bubble- Pre
Bursting- Bubble
Post- Bursting
Panel A: Overall
P/E
Mean
39.839
52.696
42.468
43.298
Median
22.890
26.590
21.220
24.660
12.857
(0.000)
2.800
(0.000)
−10.227
(0.022)
−5.370
(0.000)
0.829
(0.870)
3.440
(0.046)
No. of obs.
1,365
905
598
380
P/B
Mean
3.838
5.349
3.841
4.027
Median
2.368
2.912
2.100
2.296
1.511
(0.000)
0.544
(0.000)
−1.507
(0.000)
−0.812
(0.000)
0.186
(0.499)
0.196
(0.072)
No. of obs.
1,580
1,174
901
511
EV/S
Mean
5.816
6.656
6.308
6.594
Median
2.249
2.440
1.957
2.406
0.840
(0.027)
0.191
(0.772)
−0.348
(0.468)
−0.483
(0.025)
0.285
(0.597)
0.449
(0.049)
No. of obs.
1,580
1,174
901
511
Panel B: High-tech sector
P/E
Mean
66.684
73.411
77.375
63.073
Median
34.465
36.700
33.555
35.850
6.727
(0.479)
2.235
(0.490)
3.963
(0.777)
−3.145
(0.624)
−14.301
(0.420)
2.295
(0.865)
No. of obs.
398
288
173
123
P/B
Mean
6.068
7.730
5.382
4.996
Median
3.628
4.305
2.631
2.981
1.662
(0.008)
0.677
(0.083)
−2.347
(0.001)
−1.674
(0.000)
−0.386
(0.584)
0.350
(0.348)
No. of obs.
489
429
324
202
EV/S
Mean
5.720
7.108
7.412
4.508
Median
1.961
2.350
1.851
2.358
1.388
(0.157)
0.389
(0.028)
0.304
(0.798)
−0.499
(0.082)
−2.903
(0.012)
0.507
(0.786)
No. of obs.
489
429
324
202
Panel C: Low-tech sector
P/E
Mean
38.858
38.807
39.165
44.457
Median
23.410
23.345
20.515
23.360
−0.051
(0.994)
−0.065
(0.807)
0.358
(0.965)
−2.830
(0.224)
5.290
(0.640)
2.845
(0.545)
No. of obs.
420
296
215
102
P/B
Mean
3.717
3.907
4.204
3.144
Median
2.514
2.570
2.628
2.026
0.189
(0.667)
0.056
(0.281)
0.297
(0.574)
0.058
(0.922)
−1.060
(0.068)
−0.602
(0.053)
No. of obs.
435
326
212
105
EV/S
Mean
2.346
2.074
2.217
1.979
Median
1.163
1.304
1.187
1.196
−0.271
(0.540)
0.141
(0.307)
0.142
(0.721)
−0.117
(0.498)
−0.237
(0.530)
0.009
(0.563)
No. of obs.
435
326
212
105
Panel D: Trading & Services sector
P/E
Mean
43.934
74.679
44.631
40.61
Median
30.800
35.695
21.980
28.410
30.745
(0.000)
4.895
(0.120)
−30.048
(0.006)
−13.715
(0.000)
−4.021
(0.687)
6.430
(0.342)
No. of obs.
547
321
210
155
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9
Table 4 (Continued )
Pre
Bubble
Bursting
Post
Bubble- Pre
Bursting- Bubble
Post- Bursting
P/B
Mean
4.564
6.422
3.618
3.131
Median
3.139
3.114
2.000
2.006
1.857
(0.001)
−0.025
(0.321)
−2.803
(0.000)
−1.114
(0.000)
−0.486
(0.225)
0.006
(0.870)
No. of obs.
656
419
365
204
EV/S
Mean
2.482
5.541
3.311
1.704
Median
1.303
1.610
0.983
0.882
3.058
(0.000)
0.307
(0.005)
−2.229
(0.022)
−0.627
(0.000)
−1.606
(0.011)
−0.101
(0.335)
No. of obs.
656
419
365
204
This table presents the univariate differences between the means and the medians of transaction multiples across the four sub-periods. P/E is sale price of the firms’ equity
divided by net income before extraordinary items. P/B is sale price of the firms’ equity divided by the book value of equity. EV/S is the sale price of the firms’ equity plus
total liabilities less current liabilities divided by the firm’s total revenues. E, B and S are from the most recent fiscal year ending prior to the date of the sale transaction. In
panel A, the results are presented for the full sample, and in panels B, C and D we report results for the high-tech, low-tech and trading & services sub-samples. P-Values are
presented in parentheses.
cantly negative only for the B/P multiple. Turning to focus on the
coefficients on the sub-period indicators, we find no evidence of
a direct bubble effect on either of the transaction multiples (6
which captures the difference between the pre-bubble and the
bubble sub-periods is insignificant); i.e., it seems that transaction
multiples did not increase during the bubble, compared with the
pre-bubble period. In contrast, there is evidence (according to P/B
and EV/S ratios, but not according to P/E) of a decrease in transaction multiples at the bursting of the bubble, compared to their
levels during the pre-bubble and the bubble sub-periods. Specifically, 7 which captures the difference in multiples between the
bubble and the subsequent bubble bursting sub-periods is significantly positive, implying higher inverted multiples (and thus lower
multiples) during the bursting of the bubble. When comparing
between the coefficient on the ‘Burst’ (about 0.4) and the coefficient on the ‘PostBurst’ (about 0.3) indicator variables, we find that
the two do not differ significantly, indicating that multiples did
not change significantly throughout these two sub-periods. These
results are generally consistent with those found in the univariate
analysis.
For low-tech industries, consistent with the univariate analysis,
we do not find evidence for a direct time effect on transaction multiples (the coefficients on all three sub-period-indicator variables
are statistically insignificant for each multiple applied). For trading
& services, the multivariate analysis indicates that, in fact, a direct
effect of the bubble on transaction multiples did not occur (6 is
insignificant according to all three multiples). Like in the high-tech
sector, there is some evidence (according to P/B and EV/S ratios, but
not according to P/E) of a decrease in transaction multiples at the
bursting of the bubble, but no change between the ‘Burst’ and the
‘PostBurst’ sub-periods.
As a robustness check to our finding that transaction multiples
did not change in the bubble sub-period, we repeat the multivariate analysis excluding the PreBubble indicator variable. If indeed
multiples did not change during the bubble, compared with the
pre-bubble sub-period, then a dummy variable indicating the prebubble sub-period is, in effect, irrelevant to the model specification.
Untabulated results show that when PreBubble is excluded from
the model, Adj.R2 as well as the coefficients on all the remaining
variables and their significance levels remain generally similar (all
differences are statistically insignificant). This implied irrelevance
of an indicator variable for the pre-bubble sub-period indicates
that, consistent with the results from the univariate analysis as well
as the results from the multivariate analysis which includes the PreBubble indicator variable, the difference in multiples between the
pre-bubble and the bubble sub-periods is indeed insignificant; i.e.,
with the market bubble inflating, transaction prices do not seem
to have changed significantly. The results are found to be robust to
the multiple used and to industrial sectors.
Based on the two-stage analysis of univariate tests followed
by multivariate models, we conclude that investors outside the
exchange were not affected by the euphoric atmosphere on the
exchange during the bubble with respect to high-tech investments.
Nonetheless, the bursting of the bubble seems to have led to further
cautiousness in their decision making, and they remained cautious
even when prices on the exchange rebounded.
4.3. Price regression analysis
The results thus far indicate that valuations of transactions
outside the exchange were not affected significantly by the bubble. We now extend our analysis of M&A pricing across the four
sub-periods, focusing on the information used by investors to
value their targets. The bursting of the bubble raised the question regarding the relevance of financial statements in reflecting
the economic reality of a company’s basic business (Olstein, 2006;
Penman, 2003). We seek to explore whether financial variables
played a different role as proxies for expectations about the future
performance of a target firm in each sub-period.
We employ a price level analysis to explore changes in the relation between target prices and financial statement information
across the four sub-periods. The price regressions are based on a
version of the Ohlson (1995) model, where we regress the sale price
of the firm’s equity on the book value of equity, current earnings
and proxies for expected earnings growth (see also Collins et al.,
1997; Core et al., 2003; Dechow, Hutton, & Sloan, 1999, among others). Consistent with the literature (e.g., Collins et al., 1997; Hand,
2005), we define value-relevance as the adjusted R-square from
the regression. Upon regressing prices on the financial variables,
we separate earnings into positive and negative earnings. This differentiation between value implications of positive and negative
earnings is based on prior literature that documents differences in
the valuation of profits and losses (e.g., Basu, 1997; Collins et al.,
1997; Hayn, 1995). Following prior studies (e.g., Core et al., 2003),
we include in the model R&D expense and sales growth as proxies
for expected growth. The model we employ is:
Pit =ˇ0 +ˇ1 BVit +ˇ2 Eit +ˇ3 Neg Eit +ˇ4 R&D+ˇ5 SalesChit +εit
(2)
where P is the sale price of the firm’s equity; BV is the book value
of equity; E represents earnings before extraordinary items; Neg E
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Table 5
Multivariate analysis of transaction multiples.
Panel A: High-tech sector
Intercept
E/P
B/P
S/EV
0.081
(0.000)
0.274
(0.001)
−0.024
(0.002)
−0.076
(0.004)
−0.227
(0.000)
−0.091
(0.049)
−0.023
(0.000)
0.101
(0.336)
0.411
(0.000)
0.359
(0.015)
0.110
12.569
(0.000)
1,444
0.953
(0.000)
−0.039
(0.000)
−0.095
(0.026)
−0.497
(0.000)
−0.091
(0.000)
−0.012
(0.193)
0.129
(0.300)
0.373
(0.000)
0.262
(0.075)
0.122
14.045
(0.000)
1,444
0.785
(0.000)
−0.317
(0.000)
−0.091
(0.103)
−0.106
(0.033)
−0.059
(0.163)
−0.034
(0.054)
0.080
(0.295)
0.105
(0.241)
0.076
(0.525)
0.046
5.319
(0.000)
1,078
1.620
(0.000)
−0.071
(0.001)
−0.518
(0.000)
−0.154
(0.021)
−0.202
(0.000)
−0.035
(0.166)
0.050
(0.583)
0.070
(0.601)
0.027
(0.861)
0.073
7.614
(0.000)
1,078
0.469
(0.000)
−0.066
(0.093)
−0.052
(0.269)
−0.017
(0.559)
0.051
(0.238)
−0.024
(0.118)
−0.036
(0.609)
0.223
(0.008)
0.185
(0.070)
0.044
1.622
(0.000)
−0.332
(0.000)
−0.069
(0.466)
−0.274
(0.000)
−0.077
(0.336)
0.013
(0.653)
−0.001
(0.991)
0.392
(0.011)
0.375
(0.041)
0.065
ROE
PM
SalesGrowth
Size
Lev
PreBubble
Burst
PostBurst
Adj.R2
F-Value
# obs.
Panel B: Low-tech sector
Intercept
−0.110
(0.000)
−0.093
(0.001)
−0.004
(0.085)
0.001
(0.677)
−0.012
(0.442)
0.001
(0.886)
0.027
(0.100)
0.136
8.912
(0.000)
982
0.063
(0.000)
ROE
PM
SalesGrowth
Size
Lev
PreBubble
Burst
PostBurst
Adj.R2
F-Value
# obs.
Panel C: Trading & Services
Intercept
−0.120
(0.001)
−0.011
(0.045)
−0.065
(0.004)
−0.004
(0.253)
0.009
(0.329)
0.002
(0.896)
0.009
(0.546)
0.056
6.613
(0.000)
1,033
0.051
(0.000)
ROE
PM
SalesGrowth
Size
Lev
PreBubble
Burst
PostBurst
Adj.R2
−0.023
(0.422)
−0.026
(0.028)
0.003
(0.504)
0.001
(0.867)
−0.011
(0.159)
0.011
(0.307)
0.004
(0.718)
0.019
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11
Table 5 (Continued )
F-Value
# obs.
E/P
B/P
S/EV
3.080
(0.003)
1,233
7.438
(0.000)
1,644
10.270
(0.000)
1,644
This table presents the parameter estimates together with their significance levels for the following regression model:
ValuationRatio = ˇ0 + 1 ROE + 2 PM + 3 SalesGrowth + 4 Size + 5 Lev + 6 PreBubble + 7 Burst + 8 PostBubble + ε.
The regression is estimated separately for E/P, B/P and S/EV ratios, as well as for each sector. P/E is sale price of the firms’ equity divided
by net income before extraordinary items. P/B is sale price of the firms’ equity divided by the book value of equity. EV/S is the sale price of
the firms’ equity plus total liabilities less current liabilities divided by the firm’s total revenues. E, B and S are from the most recent fiscal
year ending prior to the date of the sale transaction. ROE is net income before extraordinary items divided by the book value of equity; PM
(profit margin) is EBITDA divided by Sales; SalesGrowth is the percentage change in annual sales; Size is the log of total assets; Leverage is
the ratio of total liabilities less current liabilities to total assets. PreBubble (Burst,PostBurst) is an indicator variable that equals 1 if the date
of the transaction falls within the pre-bubble (bursting, post-bursting) sub-period, 0 otherwise. P-Values of the coefficients are presented
in parentheses.
is negative earnings before extraordinary items, 0 otherwise17 ;
R&D is research & development expense; and SalesCh is the annual
change in sales.
Prior research advocates deflating financial data in accounting
research by a proxy for scale (rather than including a scale proxy
as an independent variable; see, e.g., Lo, 2004). The advantages
of deflation by a scale proxy include, inter alia, mitigation of hetroscedasticity, R2 bias and coefficient bias. We thus deflate Eq. (2)
by the book value of equity. All observations are conserved as our
sample is restricted to firms with positive book value.
Our deflated regression model is:
Pit
1
E
Neg Eit
R&Dit
= ˇ0
+ ˇ1 + ˇ2 it + ˇ3
+ ˇ4
BVit
BVit
BVit
BVit
BVit
+ˇ5
SalesChit
+ εit
BVit
(3)
The intercept in the deflated model can be interpreted as the
coefficient on book value of equity in the undeflated model. Consistent with prior studies, we retain the inverse of book value
of equity as an explanatory variable in the deflated regression
model. According to Core et al. (2003), the inverse of book value
of equity should be retained in the deflated regression, “because
we include the intercept in the unscaled model to explain economic variation in market values that is not captured by our other
explanatory variables.” In each regression, we include intercept
dummies for industry to control for industry fixed effects. The
regressions include White’s (1980) correction.
The results of the deflated regressions, for each sub-period separately, are presented in panel A of Table 6. The results indicate that
in the time period preceding the bubble, accounting fundamentals are value-relevant for pricing of M&A transactions. Specifically,
the signs of the estimated coefficients are consistent with prior
research; the coefficients on BV and E are significantly positive
whereas the coefficient on Neg E is significantly negative. Notably,
while the coefficient on positive earnings is, as expected, significantly positive, the coefficient on negative earnings (sum of E and
Neg E) is not significantly different from zero. The value irrelevance of negative earnings may imply investor uncertainty with
respect to the future prospects of the acquired firm; i.e., the neg-
17
We define Neg E as earnings before extraordinary items, if earnings before
extraordinary items <0, 0 otherwise. Thus, Neg E takes on only non-positive values. We also include in the regressions a dummy variable that equals 1 if earnings
before extraordinary items are negative, zero otherwise. The dummy is nonsignificant for all years (untabulated). Additionally, in an untabulated analysis, we add to
regression model (1) an interaction variable of NEG E with BV to inquire whether
the coefficient on the book value of equity is different for loss firm-years. We find
that the coefficient on the interaction variable in statistically insignificant with the
other coefficients in the model similar to those reported in our tables.
ative earnings may precede either positive future cash flows due
to the transitory nature of losses (see Core et al., 2003) or more
negative cash flows. As for SalesCh and R&D, the coefficients are positive, consistent with these variables capturing expected growth in
earnings, however statistically insignificant. During the bubble and
the bubble burst, the results show that current earnings lose their
relevance whereas variables capturing future growth in earnings
become value-relevant. Both the coefficients on SalesCh and R&D
are positive and significant at the 1% and 5% level, respectively. In
post-bursting years, earnings become value-relevant again in concomitance with SalesCh and R&D losing their significance. Hence,
the relations between financial information and equity values have
undergone unusual changes throughout the investigated period.
In particular, during extreme events that the capital market has
undergone – the bubble and the bubble bursting – investor valuations tend to base on expectations for the future rather than
on the current performance of the target firm. In the sub-periods
prior to and after these market vicissitudes, current earnings rather
than expectations for future earnings are found to contribute to the
explanation of the variation in transaction prices.
It seems that, in concomitance with the occurrence of major
events, investors experience a learning curve, which is reflected in
changes in the (importance of the) role accounting variables play
as proxies for expectations of future cash flows. Specifically, after
these events occurring, investors may be willing to attach a higher
price to a proven ability to generate higher earnings, but refrain
from the risk of attaching a higher price based on expectations
for the future. Note that the book value of equity retained its significance throughout the years. As the role of the balance sheet
versus the income statement in explaining market values depends
on investors’ perception of earnings persistence, our finding that BV
was systematically value-relevant while earnings lost and gained
relevance throughout the investigated period, may be explained by
investors suspecting lower persistence of earnings.
4.3.1. The ability of accruals versus cash flows in explaining the
purchase price
We now disaggregate the income statement into accrual and
non-accrual components. We seek to compare investor reliance on
these components in the setting of M&A transactions, where the
motivation for earnings manipulation is considerably higher. In this
setting, firm managers have incentives to take actions that increase
their sale price. If management expects price to be a positive function of earnings, firms could manage accruals upwards. Given the
high likelihood for an accrual manipulation, we seek to distinguish
between the role accruals play as proxy for expectations about the
target’s future cash flows, versus the role of the cash flow component of earnings. Using the accounting identity that net income
equals the sum of cash flows from operations and accruals, we reestimate our basic price regression model, Eq. (3), by decomposing
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Table 6
Price regressions analysis.
Panel A
Intercept
1/BV
E/BV
Neg E/BV
Sales Ch/BV
R&D/BV
Adj.R2
F-Value
Sig.
# obs.
Panel B
Variable
Intercept
1/BV
TotalAcc/BV
CFO/BV
(Loss × TotalAcc)/BV
(Loss × CFO)/BV
Sales Ch/BV
R&D/BV
Adj.R2
F-Value
Sig.
# obs.
Panel C
Variable
Intercept
1/BV
ExpAcc/BV
AbnAcc/BV
CFO/BV
(Loss × ExpAcc)/BV
(Loss × AbnAcc)/BV
(Loss × CFO)/BV
Sales Ch/BV
R&D/BV
Adj.R2
F-Value
Sig.
# obs.
Pre
Bubble
Bursting
Post
0.891
(0.000)
3.864
(0.000)
5.420
(0.000)
−5.511
(0.055)
0.048
(0.399)
0.095
(0.574)
2.070
(0.000)
11.452
(0.000)
0.174
(0.218)
−0.007
(0.429)
0.562
(0.000)
1.259
(0.023)
1.223
(0.000)
1.429
(0.000)
0.036
(0.581)
−0.012
(0.309)
0.182
(0.007)
0.801
(0.052)
0.514
(0.000)
0.405
(0.000)
5.233
(0.011)
−5.001
(0.038)
0.080
(0.241)
0.123
(0.309)
0.142
32.854
(0.000)
1,580
0.153
30.197
(0.000)
1,174
0.113
29.379
(0.000)
901
0.115
28.096
(0.000)
511
0.739
(0.000)
3.447
(0.000)
5.133
(0.000)
5.165
(0.000)
−5.005
(0.000)
−5.013
(0.000)
0.041
(0.415)
0.098
(0.483)
2.813
(0.000)
5.578
(0.000)
2.406
(0.000)
0.388
(0.253)
0.009
(0.489)
0.003
(0.632)
0.670
(0.000)
1.652
(0.000)
1.047
(0.000)
2.554
(0.000)
−0.366
(0.125)
0.433
(0.006)
0.014
(0.224)
0.006
(0.088)
0.194
(0.004)
1.066
(0.025)
0.201
(0.000)
0.870
(0.000)
5.013
(0.021)
6.114
(0.003)
−5.011
(0.055)
−6.016
(0.019)
0.053
(0.434)
0.143
(0.112)
0.138
11.427
(0.000)
1,580
0.180
14.459
(0.000)
1,174
0.121
10.851
(0.000)
901
0.127
10.186
(0.000)
511
0.366
(0.000)
0.023
(0.914)
7.288
(0.009)
5.854
(0.089)
5.426
(0.000)
−8.057
(0.002)
−6.953
(0.036)
−5.004
(0.689)
0.290
(0.141)
0.065
(0.624)
2.422
(0.000)
6.491
(0.000)
0.363
(0.218)
4.462
(0.017)
0.346
(0.345)
0.345
(0.133)
−8.843
(0.000)
0.000
(0.926)
1.437
(0.000)
1.724
(0.000)
0.618
(0.000)
1.520
(0.000)
1.980
(0.070)
−1.834
(0.023)
0.407
(0.023)
−1.837
(0.013)
−0.879
(0.013)
0.001
(0.142)
1.007
(0.009)
2.483
(0.000)
0.130
(0.000)
0.064
(0.973)
5.055
(0.007)
0.620
(0.390)
6.074
(0.009)
−6.624
(0.023)
−0.090
(0.927)
−6.004
(0.596)
0.452
(0.710)
0.760
(0.101)
0.258
18.749
(0.000)
1,580
0.203
13.463
(0.000)
1,174
0.206
11.530
(0.000)
901
0.232
9.088
(0.000)
511
This table panel A presents the results of the following regression model:
Pit /BVit = ˇ0 1/BVit + ˇ1 + ˇ2 Eit /BVit + ˇ3 Neg Eit /BV + ˇ4 R&Dit /BVit + ˇ5 SalesChit /BVit + εit .
P is the sale price of the firm’s equity; BV is the book value of equity; E represents earnings before extraordinary items; Neg E is negative earnings before extraordinary items,
0 otherwise; R&D is research & development expense; and SalesCh is the annual change in sales. In panel B, the earnings are disaggregated into accrual and non-accrual
components. Loss is a dummy variable that equals 1 if earnings before extraordinary items are negative, zero otherwise. We interact the indicator Loss variable with the
decomposed-earnings variables, total accruals (TotalAcc) and cash flows from operation (CFO). In panel C, we further differentiate between expected and unexpected accruals.
We identify unexpected – abnormal – accruals using the widely applied modified Jones (1991) model. P-Values of the coefficients are presented in parentheses.
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earnings into accruals and operating cash flows. If buyers are aware
of accrual manipulation, then the coefficient on accruals should be
lower than the coefficient on cash flows, throughout the sample
period. We estimate:
Pit
1
TotalAccit
CFOit
= ˇ0
+ ˇ1 + ˇ2
+ ˇ3
BVit
BVit
BVit
BVit
+ ˇ4
(Loss × OCF)it
R&Dit
(Loss × TotalAcc)it
+ ˇ5
+ ˇ6
BVit
BVit
BVit
+ ˇ7
SalesChit
+ εit
BVit
(4)
Loss is a dummy variable that equals 1 if earnings before extraordinary items are negative, zero otherwise. We interact the indicator
Loss variable with the decomposed-earnings variables, total accruals (TotalAcc) and cash flows from operation (CFO). This allows us
to isolate the coefficient on accruals versus operating cash flows for
profit firm-years from loss years. Additionally, estimation of Eq. (4)
for each sub-period separately allows us to explore changes in the
weight that bidders place on operating cash flows versus accruals
across the sub-periods.
Table 6, panel B contains the results of estimating Eq. (4). We
first compare the coefficient on accruals and the coefficient on operating cash flows. In the pre-bubble sub-period, the coefficient on
accruals and operating cash flows for profit firm-years is 5.133 and
5.165, respectively. The difference in these coefficients is not statistically significant. Thus, it seems that prior to the bubble, bidders
placed similar weight on accruals and on operating cash flows. During the bubble, the coefficient on accruals is lower (2.406), however
remains highly significant. In contrast, the coefficient on operating cash flows (0.388) becomes insignificant. In essence, investors
seem to attach higher value to the accrual component of earnings,
which may be subjected to manipulation by management, than
to cash. This result is consistent with the euphoria – and hence
to less rationalism in investors’ decision making – that characterized this sub-period, which is expressed in reduced awareness
of investors to the risk of lower earnings quality for target firms
prior to the sale transaction. Interestingly, the opposite result is
obtained for the bubble bursting sub-period. While the coefficient
on accruals changes its sign (−0.366) and loses its significance, the
coefficient on cash flows (0.433) becomes significant at the 1% level.
This finding implies that investors undergo a learning process in
terms of the type of variables preferred, and in our case, appear to
be more cautious and suspicious with regards to earnings reported
prior to the transaction. In the post-bursting sub-period, the coefficient on accruals and operating cash flows for profit firm-years is
5.013 and 6.114, respectively. The difference in these coefficients
is statistically significant, implying that in post-bursting years,
bidders place a higher weight on operating cash flows than on
accruals.
For loss firms, the results indicate that, prior to the bubble as
well as in post-bursing years, the coefficients on accruals and operating cash flows are not significantly different from zero, consistent
with the findings for negative earnings in Eq. (3). During the bubble, we do not find a difference between the coefficients on accruals
and operating cash flows in profit versus loss firms. That is, the
coefficient on accruals (cash flows) is (in)significantly positive for
loss firms as it is for profit firms. In contrast, during the bursting
of the bubble, the coefficient on operating cash flows in loss firms
is significantly higher than that in profit firms, possibly due to the
transitory nature of losses.
Note that the coefficients on the accrual and cash flow components of earnings are lower across the drastic bubble and bursting
sub-periods in comparison to the pre- and post-bursting subperiods. In all, it seems that the use investors make of accounting
13
variables in valuations of target firms is a dynamic process which
changes over time.
We now move to differentiate between expected and unexpected accruals. We identify unexpected – abnormal – accruals
using the widely applied modified Jones (1991) model. Kothari,
Leone, and Wasley (2005) explain that earnings management is
related to firm performance (i.e., firms with extreme financial performance are likely to engage in earnings management) and thus
the impact of performance on accruals should be accounted for
when estimating abnormal accruals. In keeping with Kothari et al.,
and consistent with prior studies (e.g. Raman & Shahrur, 2008),
we include a proxy for performance – return on assets (ROA) –
as an independent variable in the modified Jones model.18 Prior
research also suggests that firms with higher growth opportunities tend to have higher accruals (e.g., Cohen, Dey, & Lys, 2008;
McNichols, 2002). We thus control for growth options in the modified Jones model by including the book-to-market ratio (see also
Raman & Shahrur, 2008).
We estimate the following cross-sectional regression for each
two-digit SIC industry and year:
TotalAcct
1
+ ˇ1
= ˇ0
TAt−1
TAt−1
REV
t
TAt−1
+ ˇ3 ROAt + ˇ4 BMt + εt
−
ARt
TAt−1
+ ˇ2
GPPEt
TAt−1
(5)
where TA is total assets, REV is the change in revenues from the
previous year, AR is the change in accounts receivable, GPPE is
gross fixed assets, ROA is net income before extraordinary items
scaled by lagged total assets and BM is the ratio of total assets to
total assets minus book value of equity plus market value of equity.
Consistent with prior research, total accruals are net income minus
cash flows from operations. The residual in the regression model
(ε) is the measure of unexpected – discretionary – accruals. These
accruals indicate the extent to which a firm manages its earnings
(Dechow & Skinner, 2000).
Table 6, panel C contains the results of estimating Eq. (4)
with total accruals decomposed into expected and unexpected
accruals. For pre-bubble years, we find that the coefficient on
expected accruals (7.228) is significantly higher than the coefficients on unexpected accruals and operating cash flows (5.854
and 5.426, respectively). During the bubble, the coefficient on
unexpected accruals is significantly positive (4.462) while the coefficients on expected accruals and operating cash flows (0.363 and
0.346, respectively) are insignificantly different from zero, implying that acquisitions during the time of the bubble were affected
by the noisy, less persistent component of earnings. Markedly,
this has changed when the bubble burst, as reflected in a significantly negative coefficient on unexpected accruals (−1.834) versus
a significantly positive coefficient on expected accruals and on
cash flows (1.980 and 0.407, respectively). In effect, the significantly negative coefficient on unexpected accruals implies that
during the bursting of the bubble, investors discounted the price
they were willing to pay once detecting upward earnings manipulation. This further demonstrates the process of learning that
investors undergo in concomitance to processes in the market.
In post-bursting years, the coefficient on unexpected accruals is
insignificant, implying that investors remain cautious (in the prebubble and during the bubble, this coefficient was significantly
positive). The coefficients on expected accruals and cash flows are
significant (5.055 and 6.074, respectively).
18
Kothari et al. (2005) show that matching based on current ROA performs better than matching based on prior year’s ROA. The performance-matching approach
distinguishes between ‘normal’ and ‘abnormal’ EM.
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5. Summary and conclusion
We document a considerable increase in the prevalence of M&A
transactions during the bubble for all sectors, followed by a reduction to pre-bubble levels at the bursting of the bubble, and further
reduction in subsequent post-bursting years, despite recovery in
the capital markets during that time. Although the frequency of
M&A increased during the bubble, the pricing of M&A did not
change. In contrast, the bursting of the bubble seems to have led to
further cautiousness by these investors, which extended through
post-bursting years, even when prices on the exchange rebounded.
While we do not find robust evidence for changes in price multiples outside the exchange in concomitance with the changes
on the exchange, we document changes in the information used
by investors to value their targets. Specifically, during the bubble
and the bursting of the bubble, investor valuations tended to rely
on expectations for the future rather than on the current performance of the target firm, and vice versa for the pre-bubble and
the post-bursting sub-periods. Additionally, our evidence suggests
that during the euphoric bubble sub-period, investors outside the
exchange did take more risks, relying on the noisy, less persistent
component of earnings – unexpected accruals – rather than on
expected accruals and cash flows. However, these investors seem
to have undergone a learning process, appearing to be more cautious since the bursting of the bubble with regards to the quality
of earnings reported prior to the transaction. Hence, this process of
learning that investors outside the exchange undergo in concomitance to processes in the market results in these investors being
less affected by periodical or cyclical sentiments of euphoria and
depression in the capital market.
An important implication of the findings of this study is that,
although the pricing of a target firm in an acquisition outside
the exchange is intended mainly to the investors involved in the
transaction,19 it in effect provides outside investors with a good
firm value indicator. Namely, the multiple derived from the transaction may serve as a good benchmark multiple for investment
decision making, one to be compared with the industry multiple
– which is derived from stock exchange prices of the firms in the
industry – or from target prices published by sell-side analysts.
Notably, an M&A multiple may provide a moderate benchmark
which is less affected by the “mood” of the market.
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