Realización personal (RP)

Extant literature is concerned with the understanding of motives and consequences of several deal characteristics in M&As, some of which are considered as ‘treatments’ to certain issues (i.e. asymmetric information). Within the M&A context, the impact of such treatments on the success of the deal is, therefore, investigated by analyzing the acquiring firm’s short-run abnormal returns as the response random variable (i.e. the outcome variable). In this paper we investigate the impact of two treatments on the deal’s likelihood of success: EP-financing and FA-presence. Both ‘treatments’ have been illustrated to lead to positive acquirer short-run abnormal returns. Therefore, concerns are raised regarding the potential presence of selection-bias that could reduce the strength of our derived conclusions regarding the impact of each treatment on acquirers’ value gains, or mislead them.

Specifically, selection-bias may occur when the analysis of an outcome is conditional on the choice of a variable/treatment that is endogenous to the outcome. Within the context of our study, we argue that M&As can be initiated by either an acquirer, or the FA who either seeks buyers for firms wishing to be sold, or proposes potential targets to upcoming acquiring firms, either because she was instructed to by the to-be acquirer, or under her own initiative. Moreover, when a deal is not initiated by FAs (although FAs could appear later) an EP-payment could be the financing mechanism that the merging firms have already agreed upon, or endorsed by the FAs who might acknowledge its suitability. As a result, considerable selection-bias concerns

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regarding EP-financing and FA-presence are raised that need to be addressed appropriately in order to ensure the robustness of our conclusions. The PSM method, augmented with the Rosenbaum-bounds (RB) method, allows for an unbiased causal inference and addresses such selection-bias concerns (Dehejia and Wahba, 2002).

This is achieved by matching treated to untreated (or control) M&As based on a single propensity score that is estimated at deal level using observable merging firm- and deal- specific pre-treatment characteristics. Specifically, we model the probability of choice of the treatment, which consists of EP-financing accompanied by FA-presence (EPFA) via a logit model and estimate each treated and control deal’s propensity score of choosing the treatment. Subsequently, we match treated deals to their closest, in terms of propensity score, control ones and observe the difference in the outcome variable (CAR). We employ 1-to-1 nearest neighbor matching with replacement within 1% of Absolute Probability Difference (APD). Moreover, as PSM is based on matching relative to each deal’s propensity score to exhibit the treatment, and not on each deal’s separate covariate’s effect on the probability of its occurrence, we test for covariate balance between ‘treated’ and ‘control’ deals once matching is complete, as a robustness check. Rosenbaum and Rubin (1985) illustrates that a two sample t-test among the distribution of covariates between the ‘treated’ and ‘control’ groups constitutes a sufficient diagnostic to determine covariate balance.30

Accordingly, in order to address the above concerns regarding the wealth effects of FA- presence in EP-financed deals, we employ a three-Exercise procedure. Each Exercise involves matching our EPFA-deals to different groups of counterfactual deals, thus enabling us to robustly estimate the treatment effect of: (a) the impact of FA-presence on EP-financed deals, (b) the impact of EP-financing on deals advised by FAs and, (c) the impact of the joint presence of FAs and EPs (EPFA) on acquirers’ short-run abnormal returns, relative to counterfactuals that do not exhibit neither the presence of FAs nor EPs. Specifically, Exercise 1 involves the selection of counterfactuals from a control portfolio that exhibits only the presence of EPs, but not FAs (EPNFA). This procedure is enabling us to identify the impact of FA-presence within the treated group (EPFA), or to decompose the impact of FAs from EPFA-deals (i.e. FA = EPFA – EP).

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Exercise 2 involves the selection of counterfactuals from a control portfolio that exhibits only the presence of FAs, but not EPs (FANEP). This procedure is enabling us to identify the impact of EP-financing within the treated group (EPFA), or to decompose the impact of EPs from EPFA- deals (i.e. EP = EPFA – FA). Finally, Exercise 3 involves the selection of counterfactuals from a control portfolio that exhibits neither the presence of FAs nor EPs (NFANEP). This procedure is enabling us to identify the aggregate impact of the simultaneous presence of both FAs and EPs (EPFA) on acquirers’ short-run wealth gains.

Moreover, based on the estimated propensity scores (via each matching exercise’s logit model) we further identify the ‘optimal’ choice of the treatment within each exercise. The proportion of treated deals in each exercise constitutes the ‘a priori’ probability (threshold or cut- off point) of a deal belonging to the treated group and allows the classification of each treated observation (EP-financing, FA-presence, EPFA-occurrence) as ‘optimal’, relative to its propensity score. A treatment (EP-financing, FA-presence, EPFA-occurrence) is classified as ‘optimal’ if the propensity score, at deal level, exceeds the a-priori probability of treatment- involvement within each matching exercise, separately. Subsequently, we investigate whether the ‘optimal’ choice of: (a) FAs within EP-deals (Exercise 1), (b) EPs within FA-deals (Exercise 2) and (c) both EPs and FAs (=EPFA) based on Exercise 3, yield significant wealth effects to acquirers’ shareholders.

Nevertheless, matching based on observed covariates may leave out potentially unobserved covariates and, hence, treated and control groups would not be necessarily comparable (i.e. PSM fails to accurately identify a ‘control’ deal that corresponds to a ‘treated’ one). 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 accommodate 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 sensitivity method allows us to examine the sensitivity of our conclusions, derived from matching, to the impact 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. Specifically, the RB method measures the degree of departure from random assignment of the treatment, which allows us to gain confidence

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regarding the strength of our derived conclusions from the PSM analysis. To this end, RB is used as a robustness check to ensure that our logit models produce estimates that are likely to be free of hidden-bias, or to ensure that our estimates are not likely to be sensitive (or how sensitive they are) to hidden-bias, caused by omitted covariates from our logistic models (Rosenbaum, 2002).

The RB sensitivity analysis illustrates that two deals may in fact not be comparable, due to the unobserved impact of one or more influential covariates but, nevertheless, such non- comparison can be measured, or controlled, by the size of a parameter 𝛤, where 𝛤 ≥ 1. Specifically, two deals, 𝑖 and 𝑗, with the same observed covariates, 𝑥_𝑖 = 𝑥_𝑗, have odds of treatment 𝜋_𝑖⁄(1 − 𝜋_𝑖) and 𝜋_𝑗⁄(1 − 𝜋_𝑗) that differ, at most, by a multiplier of 𝛤 regarding their probability of receiving the treatment:

1 𝛤≤

𝜋𝑖⁄(1 − 𝜋𝑖)

𝜋_𝑗⁄(1 − 𝜋_𝑗)≤ 𝛤 𝑤ℎ𝑒𝑛𝑒𝑣𝑒𝑟 𝑥𝑖 = 𝑥𝑗 (3)

When 𝛤 = 1 in (3) it can be asserted that two matched deals are indeed comparable, while values of 𝛤 ≥ 1 indicate the presence of some bias due to failure to control for one or more influential covariates. 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 omitted 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 negligible. Evidently, to ensure that our logit models’ estimates and, thus, the estimation of propensities are less likely to be exposed to omitted variable bias, or hidden bias, the RB sensitivity method is utilized, proposing the selection of the least exposed to hidden bias model.31

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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.

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