3.4. DATA AND METHODOLOGY
3.4.1. Data
The initial sample contained 4,324 acquisitions of U.K. public firms announced between January 1,
1985, and December 31, 2014, extracted from Thomson One Mergers and Acquisitions Database.
The Database contains deal-related information, including the deal number, the DataStream Code of
the firm, which is used to match a firm’s accounting data from DataStream, the transaction value, the
shares percentage acquired by the bidder during the course of the transaction and share percentage
the bidder owned after the transaction, which will aid in determining whether a firm’s control has
been transferred in a transaction, the payment method, deal choice, deal type, deal attitude, SIC code
of the firm from which it can be judged whether or not the deal is diversified.
The sample involved 3,078 U.K. domestic acquisitions and 1,246 cross-border acquisitions into the
United Kingdom. Deals with a missing offer premium has been cleaned, yielding 1,826 acquisitions.
Observations with missing value of bidder 5-day CARs around the announcement date were excluded,
which left a sample of 1,435 acquisitions.34 Acquisitions with the information of payment method were required, which left a sample of 1,212 acquisitions. Variables of both bidder and target firm
characteristics used in the regressions were not a missing value, which resulted in a final sample of
606 acquisitions, with 451 domestic acquisitions and 155 cross-border acquisitions.
34 The sample size reduces mostly because of bidders from countries without stock market returns, which is in line with
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Table 3.1 depicts the summary statistics for a sample of 606 public acquisitions studied in this chapter.
The mean value for the deal was $606.4 million. Of these 606 acquisitions, 380 were all-cash
acquisitions, 108 were all-stock acquisitions. There were 292 diversified acquisitions, 25 hostile
acquisitions, and 408 tender offers in the sample.
Figure 3.1 plots a time-series of number of deals for the full sample, the U.K. domestic sample, and
the cross-border sample. Overall, the sample period analysed covered the fifth and the sixth merger
waves, from 1993 to 1999, and started from 2003, respectively. The number of deals for the three
samples share a similar trend over the sample period. The number of deals increased throughout the
1990s, and peaked in 1999. They started to increase from 2003 after a sharp decrease in 2000, the
year the stock market crashed. The number of deals increased for several years from 2003 and 2006
before they declined in 2007.
Figure 3.2 plots the time-series of total deal value for the full sample, the U.K. domestic sample, and
the cross-border sample. The overall trend of the total deal value is consistent with that reported in
Figure 3.1. Interestingly, the overall trend of the total deal value of the cross-border acquisitions has
changed more dramatically than that of the domestic deals, and is larger than that of the domestic
sample in certain periods, such as 1999 to 2000, 2005 to 2007, and 2012 to 2013.
Table 3.2 shows the distribution of bidder nations. The sample consists of bidders from 13 countries.
Amongst them, a large majority are U.K. bidders, taking up 74.42% of the full sample. Of 451 cross-
border acquisitions, 67 acquisitions involve U.S. bidders who dominate foreign bidders in the cross-
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same language and have a similar cultural background to the U.K. firms are more likely to undertake
an acquisition of a U.K. target.
The thesis defines the offer premium (the target 52-week high) as the logarithmic term difference
between the offer price (the target’s highest stock price over 335 calendar days ending 30 days prior
to the announcement date) and target stock price 30 days prior to the announcement date.35 The measure of offer premiums reflects the target expected gains during M&As, a concept which has been
widely used in related literature (Betton et al., 2009, Eckbo, 2009).36
A histogram of the difference between the offer premium and the target 52-week high, as shown in
Figure 3.3. The x-axis is in the range of -500% to 500%, and the y-axis shows the density. The shape
of the histogram indicates the extent to which the target 52-week high approaches the offer premium
within the sample.
The reference point effect on offer premiums was studied by controlling for a series of deal, bidder,
and target characteristics, as reported in Table 3.3. Edmister and Walkling (1985) documented that
cash payments are associated with higher offer premiums compared with stock payments, since
targets require higher offer premiums to compensate for the tax expense generated by capital gains.
Faccio and Masulis (2005) suggest that diversified mergers lead to greater uncertainty about bidder
35 Offer Premiums = log (Offer Price
i,t) – log (Stock Pricei,t-30). Target 52-week high = log (Target 52-week Highest Stock
Pricei,t-30) – log (Stock Pricei,t-30). The next trading day’s stock price was used when an offer was announced at a weekend.
The logarithmic term was used to counter the positive skewness bias of offer prices.
36 Prior research measures offer premiums with target abnormal returns (Schwert, 1996) questioned by Eckbo (2009). The
results are robust when a wide range of measures used for offer premiums, including target abnormal returns similar to Schwert’s approach and the target’s price in a week and 3 months before the announcement date.
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value, which implies that targets should be reluctant to accept risky offers unless higher offer
premiums are offered in compensation. A positive relationship between offer premiums and hostile
acquisitions is expected, since bidder managers pay higher offer premiums to persuade target
managers to accept an offer that may risk their professional career following a takeover. Bradley et
al. (1988) suggest that tender offers are associated with higher offer premiums since payment for the
manager goes to the shareholder.
Relative size is the deal value divided by the bidder market value. The market value (MV) is defined
as the current share price multiplied by the number of ordinary shares, expressed in the logarithmic
form. The market-to-book value (MTBV) is defined as the market value of the ordinary (common)
equity divided by the balance sheet of ordinary (common) equity in the company. Target volatility is
the standard deviation of target daily returns over the 335 calendar days ending 30 days prior to the
announcement. Run-ups are the pre-bid run-up prices calculated from 365 calendar days prior to the
takeover announcement date to seven calendar days before the takeover announcement date [-365, -
7]. All continuous data were winsorised at 1% and 99% levels to eliminate the outlier effect that both
extremely small or larger figures bias our results.
Relative size is defined as deal value divided by bidder’s market value, which measures the deal scale.
Previous research has highlighted a significant impact of the size effect on the offer premium (Asquith
et al., 1983; Dong et al., 2006). Firm size was measured as logarithmic term of market value of firms.
The mean value for firm size in the sample is larger for bidders than targets, 6.588 to 4.894, suggesting
that takeover bidders are generally stronger than their targets. This result is consistent with prior
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premium and bidder size, and a negative relationship between the offer premium and target size are
expected. According to Moeller et al. (2004), larger bidders tend to pay more for targets as they are
concerned with fewer restrictions in utilising the firm’s resources and thus become overconfident.
Alexandridis et al. (2013) suggest a robust negative relationship between the offer premium and target
size, implying that bidders’ acquisitions of a larger target are followed by a more complex process
for synergy creation, leading bidders to pay for targets with lower offer premiums in exchange for
expected synergies.
Firm’s growth opportunities were measured with market-to-book value (MTBV), which is consistent
with Rau and Vermaelen (1998). A higher MTBV indicates that the firm has a better investment
opportunity, whilst a firm with a lower MTBV suggests that the firm is short of investment
opportunities. Mean (median) bidder market-to-book value for the sample is on average higher than
that of the target, which suggests acquisitions involve a higher growth opportunities bidder and a
lower growth opportunities target. This is also predicted by the Q hypothesis of M&As (Lang, Stulz,
and Walkling 1989). A higher Q bidder is more likely to create synergies through acquiring a lower
Q target, thus it is willing to pay higher offer premiums for the targets. It is expected that offer
premiums are positively correlated to the bidder MTBV and are negatively correlated to the target
MTBV. A similar prediction can be made when the MTBV is a proxy for the firm’s valuation as per
Dong et al.’s work (2006). Thus, a high MTBV firm indicates that the firm is likely to be perceived
as a more overvalued firm whilst a low MTBV firm suggests the firm is likely to be perceived as
undervalued. It should be expected that a higher MTBV bidder in an attempt to dilute their
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undervalued (i.e. the lower MTBV targets). Therefore, in both cases, the offer premium is positively
(negatively) related to the bidder (the target) MTBV.
Pre-takeover price run-ups of both the bidder and the target (RunUps) were also controlled for while
analysing the reference point effect on the offer premium. Schwert (1996) found that the target price
run-ups increase the takeover costs of the bidder, indicating a dollar increases in target price run-up
leads to an increase of 1.13 dollars for the offer premium. It is suggested that the price run-ups are a
result of the market-wide valuation, leading the researcher to control for both the bidder and target
price run-ups. A mean (median) price run-up for the bidder is higher than that for the target. In
addition, the chapter accounts for the target volatility (Volatility) using the standard deviation of
target daily returns for the 335 calendar days ending 30 days prior to the announcement date.
Volatility of the firm represents the information asymmetry of the firm. A firm with high information
asymmetry tends to signal to the market that the firm is associated with high risks. Therefore, it can
be expected that the bidder tends to pay a higher offer premium when the true position of the target
firm is hard to justify.
The reference point effect on bidder announcement returns has been investigated by controlling for
a set of deal and bidder characteristics, since these factors have significant impacts on bidder
announcement returns, which are documented in prior M&A literature (Travlos, 1987; Rau and
Vermaelen, 1998; Alexandridis et al., 2010). More specifically, the method of payment for
acquisition (i.e. whether a means of payment for finance an acquisition is purely financed by stocks,
or cash), deal relatedness (i.e. whether or not the two firms involved in an acquisition is in the same
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were also accounted for. As to bidder characteristics, bidder size, bidder growth opportunities and
bidder pre-takeover period price run-ups were also taken into consideration.
Table 3.4 reports the mean value of a list of variables for two samples studied in this chapter: the
cross-border and the domestic acquisitions. The mean offer premium paid by the cross-border bidder
is 34.7%, which is significantly higher than that paid by the domestic bidder (27.6%), with a mean
difference of 7.1% at 1% significance level. There is no significantly difference for bidder abnormal
returns calculated by either the market-adjusted model or the market model. The mean value for the
target 52-week high is 27.7% for the cross-border acquisition sample, which is 4.4% higher than for
domestic acquisitions, suggesting that the reference point effect is more pronounced in the cross-
border acquisition sample than in the domestic acquisition sample. The cross-border bidder is found
to be stronger than the domestic bidder, reflected in significantly larger MV and higher MTBV than
those of the cross-border acquisition sample, suggesting that cross-border bidders who are more
disadvantageous in terms of information than domestic bidders should be strong enough to overcome
information asymmetry. The statistics further indicate that the information asymmetry for the cross-
border bidders tends to be larger than for their domestic counterparts.