Rosenbaum-Bounds (RB)

Matching based on the observed covariates may leave out potentially unobserved covariates and, consequently, treated and control groups would not be comparable. This criticism can be dismissed in a randomized experiment, as randomization tends to balance unobserved covariates, but it cannot be dismissed in an observational study. In order to formalize such arguments, one needs a way of determining the degree to which deals that seem comparable are, in fact, not comparable (Rosenbaum-bounds method; Rosenbaum, 1987). The RB method permits us to examine the sensitivity of our conclusions, derived from matching, to the effect of an unobserved covariate from our propensity score estimator (logit model) and enables us to measure how influential a confounding (unobserved) covariate needs to be in order to invalidate the effect of the treatment on the response random variable (announcement period CAR). Specifically, the RB method measures the degree of departure from random assignment of the treatment. This allows us to gain confidence regarding the validation of our conclusions from the matching sequence.

To this end, RB is used as a further robustness check to ensure that our logit models produce estimates that are free of hidden-bias due to misspecification errors, which are likely to appear due to omitted covariates, or to ensure ourselves that our estimates used in the matching exercises are not sensitive (or how sensitive they are) to hidden-bias caused by omitted covariates in our logit models (Rosenbaum, 2002).

Specifically, the RB sensitivity analysis illustrates that two deals may in fact not be comparable, due to unobserved parameters but, nevertheless, this non-comparison can be controlled for, to an extent, by a parameter Γ ≥ 1. Specifically, two deals, i and j, with the same observed covariates, 𝑥_𝑖 = 𝑥_𝑗, have odds of treatment _1−ππi

i and

πj

1−πjthat differ, at most, by a multiplier of Γ regarding their probability of receiving the treatment:

1 Γ≤ πi 1−πi πj 1−πj ≤ Γ whenever 𝑥𝑖 = 𝑥𝑗 (4)

When Γ = 1 in (4) it can be asserted that two matched deals are indeed comparable, while values of Γ greater than 1, Γ ≥ 1, indicate the presence of some bias due to failure to control for omitted

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covariates. The RB method is based on examining how such inferences would change. Increasing Γ and testing whether the treatment effect (the difference in the outcome variable i.e. the acquiring firms’ announcement period CAR between treated and control groups) becomes insignificant provides an adequate process to test for the existence and severity of potential hidden variable bias. This enables us to deduce the range of possible p-values for a specified Γ and estimate the cut-off point of the RB method beyond which the p-values and, hence, the treatment effects, become insignificant. On the other hand, in the case of insignificant treatment effects the RB method tests how sensitive the latter are to becoming negative and significant. Evidently, to ensure that our logit models’ estimates and, thus, the estimation of propensities are free of hidden bias due to potentially unobserved covariates, the RB method is utilized proposing the selection of the least exposed to hidden bias model. 16

5.4. Data and Results

5.4.1. The Sample

The sample consists of completed M&A deals announced by UK public firms between 01/01/1985 and 31/12/2013 and recorded by the Security Data Corporation (SDC).17 SDC records 31,828 M&A deals involving UK public acquirers within the sample period covered. In order for a deal to remain in the sample, it must meet the following criteria: first, the acquirer is a UK public company listed in the London Stock Exchange (LSE) and has a market value of at least $1m, measured four weeks prior to the announcement of the deal. To avoid the insignificant effects of very small deals, the transaction value needs to be at least $1m. Because we wish to study transactions clearly motivated by changes in control, we follow Rossi and Volpin (2004) and focus on mergers and acquisitions of at least 50 percent of the target firm’s equity (business combinations in which the total number of companies decreases after the completion of the transaction). Targets of all listings (public, private and subsidiary) and domicile (UK or non-UK) are included in the sample. To avoid the confounding effects of multiple deals, deals announced

16

An alternative method to assess the extent of selection bias within our results would be to conduct a Heckman two-stage correction method (Heckman, 1979). Nevertheless, our sample of M&A deals is composed, to a large extent, of deals involving private targets for which public information on observed lagged variables, which are frequently used as instruments in such methods, is very limited. Thus the use of the PSM technique is preferred. Moreover, to account for the potential effect of unobserved covariates, the Rosenbaum bounds sensitivity analysis is implemented.

17

The starting date of the sample is guided by the comprehensiveness of SDC. Netter, Stegemoller and Wintoki (2011) suggest that SDC offers complete coverage of M&A announcements by at least 1989.

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within 5-days surrounding another bid by the same acquirer are excluded from the sample. Furthermore, the daily stock price and market value of the acquirer need to be available from Datastream. Buy-backs, repurchases, exchange offers, recapitalizations, privatizations, self- tender offers, spin-offs and reverse takeovers are excluded from the sample. Cases where either acquirer, or target firms are government organizations are excluded from the sample. The above criteria are satisfied by 5,495 deals and remain in the sample. Cross-border M&A cases consist of 1,693 deals, 453 of which are EC-financed.

5.4.2. Sample Characteristics

Table 1 illustrates the distribution of all, domestic, and international M&A deals (Panel A), further categorizing the latter based on the extent of the acquiring firm’s multinational network (FT, NFT_NEW and NFT_SAME) and presents summary statistics on the above for all deals (Panel B), as well as for EC-financed deals specifically (Panel C). Consistent with Faccio and Masulis (2005) and Draper and Paudyal (2006), Panel A shows that the vast majority of UK M&As involve unlisted firms (85.60% of all domestic deals and 86.24% of all CBA deals respectively), while cash and mixed payments dominate the acquisitions’ financing currencies (37.64% and 24.22% respectively). Regarding the target’s domicile, roughly 30% of targets within our sample reside beyond UK borders, while almost 60% operate within a Common Law legal framework. One in five CBA deals (20.08%) constitutes an acquiring firm’s initial international expansion. Subsequent international expansions are mostly observed within countries in which the acquiring firm has already engaged in a CBA deal in the past (50.26% of all CBA deals), while non-initial international expansions in a new country account for roughly 30% of all CBA activity. Consistent with previous studies on EC-use (Barbopoulos and Sudarsanam, 2012), roughly 28% of all deals and 27% of all CBA deals within our sample involve the use of contingent earnout payments as their transaction currency. Within CBA deals, EC-use is observed in almost one in four FT deals (24.12%), while its frequency in NFT_NEW and NFT_SAME deals reaches 30.28% and 25.73%, respectively. Cash-financing constitutes the most frequent payment method in CBA deals, consistent with Moeller et al. (2005). Lastly, roughly half of our sampled M&As account for diversifying deals, irrespective of the target firm’s domicile.

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(Insert Table 5.1 about here)

Panel B illustrates the increased average size of CBA deals accounting for more than twice the size of domestic deals ($265m and $121m respectively). CBA deals also involve larger acquirers, on average, relative to domestic deals ($3,455m and $943.7m respectively). Nevertheless, not all CBA deals appear to share the above characteristics as FT deals are substantially smaller in size ($34m on average), whereas the average size of subsequent international expansions in either a new country or not increases almost tenfold ($333m and $316m respectively). Similarly, FT deals involve much smaller acquirers than NFT_NEW and NFT_SAME deals ($298m compared to $3,121 and $4,913m respectively). Yet, FT deals exhibit the greatest average and median relative deal size (0.72 and 0.11) compared to domestic (0.43 and 0.09), NFT_NEW (0.12 and 0.04) and NFT_SAME (0.22 and 0.03) deals. The above further corroborate the increased risk faced by acquirers in their initial, relative to their subsequent, international takeovers, as well as relative to domestic deals. Furthermore, the above also suggest the potential existence of agency problems in non-FT deals, signaled by the large size of the involved acquirers. Lastly, FT acquirers exhibit the greatest cash ratio and the lowest debt-to- equity ratio, relative to domestic, NFT_NEW and NFT_SAME acquirers. The above indicate the absence of liquidity and leverage considerations, which could negatively affect the likelihood of success of a firm’s initial international expansion.

In Panel C we focus on EC acquirers specifically. As in Panel B, EC-financed FT deals are, on average, smaller in size than EC-financed NFT_NEW and NFT_SAME deals ($21.66m compared to $24.36m and $54.6m respectively), involve smaller acquirers ($207m compared to $1,100m and $1,758m respectively) and incorporate greater valuation risk, as approximated by their increased relative deal size (0.31 compared to 0.08 and 0.13 respectively). This increased risk is also reflected by the average relative earnout value (=value of earnout component over deal value)18 of EC-financed FT deals when compared to their non-FT CBA counterparts (0.44 compared to 0.36 and 0.37 respectively). In contrast to reported statistics in Panel B, acquirers involved in EC-financed FT deals exhibit larger average market-to-book values than their counterparts in NFT_NEW and NFT_SAME EC-financed deals. As EC acquirers consist of

18

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mainly small firms, the above observation likely indicates their increased growth potential when engaging in FT deals, relative to acquirers not using ECs in FT deals or relative to acquirers in subsequent CBA deals. Lastly, acquirers in EC-financed FT deals also exhibit higher liquidity and lower leverage ratios than their domestic counterparts, further suggesting the absence of such concerns that should render target firms, and especially foreign ones, more reluctant towards engaging in a contingent payment structure.