Propensity Score Matching (PSM)

Observational studies differ from experimental ones in that randomization is not used to assign treatment. Within the M&A context, extant literature is concerned with the understanding of motives and consequences of several events occurring (treatments) during the deal process by examining the acquiring firms’ announcement period abnormal returns as the response random variable (outcome). This paper aims to further explore the wealth effects of EC-financing on the distribution of acquirers’ announcement period abnormal returns in international corporate takeovers. Nevertheless, ECs are used in a small proportion of our large sample of M&A transactions. This raises concerns as to whether sample-selection bias reduces the reliability of our derived results and conclusions from both the univariate and multiple regression analyses. Evidently, addressing such concerns is vital in order to clarify the impact of EC-financing on acquirers’ short-run wealth gains. Specifically, the MNH indicates that acquirers should reap greater abnormal equity gains when expanding internationally for the first time, as well as when expanding internationally subsequently but in a new country. It, therefore, needs to be determined whether the wealth effects generated by EC-financing in FT and NFT_NEW deals reflect the impact of EC-use on the acquisition’s expected synergy potential, and not solely the effect of the expansion of the acquiring firm’s multinational network. The PSM methodology can

138

help us address these concerns and enhance our understanding of the wealth implications of EC- financed cross-border acquisitions of firms.15

Implementing the PSM methodology allows for an unbiased causal inference, by pairing treated (EC-financed) and comparison/control (non-EC financed) sample units based on observable pre-treatment characteristics and observing differences between the two groups in a response random variable (announcement period CAR) (Dehejia and Wahba, 2002). Specifically, PSM involves matching (treated) deals that exhibit a certain attribute (treatment), i.e. EC- financing, to counterfactual deals (controls) that do not exhibit the treatment but illustrate the same propensity score (probability) to do so as the treated deals that actually do. We employ PSM in two Exercises. In Exercise 1, we match EC-financed FT deals to NEC-financed FT deals. In Exercise 2, we match EC-financed NFT_NEW deals to NEC-financed NFT_NEW deals. These two matching exercises enable us to address potential self-selection concerns and accurately estimate the effect of EC-financing on acquirers’ short-run wealth gains which is now highly likely to be bias-free. We employ 1-to-1 nearest neighbor matching with replacement within 1% of Absolute Probability Difference (APD).

Lastly, as PSM is based on matching relative to each deal’s probability to exhibit the treatment (calculated from the propensity score estimator, i.e. the logit model) and not on each deal’s separate covariate’s effect on the latter, we test for covariate balance between treated and control deals once matching is complete, as a robustness check. Rosenbaum (1985) illustrates that a two-sample t-test among the distributions of covariates between the treated and control groups constitutes a sufficient diagnostic to determine covariate balance.

5.3.4. Determinants of Earnout Choice

The PSM method is based on matching treated to counterfactual sample units based on a propensity score predicting the use of the treatment. Therefore, the logistic regression methodology is implemented in order to model the choice of ECs in FT (Exercise 1) and NFT_NEW (Exercise 2) deals and calculate each deal’s propensity to exhibit the treatment (EC). Specifically, the logit model estimates the probability of a sample deal being financed with an

15

Behr and Heid (2011) provide a thorough analysis of the PSM methodology along with its application in evaluating the success of German bank mergers in the period 1995-2000.

139

EC, conditional upon merging institution- and deal- specific characteristics, and may be regarded as “predicting” the use of ECs conditional on these characteristics. Therefore, in the estimations, our dependent variable assumes the value of 1 if a deal is EC-financed and 0 otherwise. The binary variable is regressed, in a logistic regression framework, against a set of independent variables.

Current literature on EC-financing illustrates that earnout provisions are most likely to be observed in acquisitions of unlisted firms, operating in intangible-rich sectors, or unrelated industries, and characterized by substantial risk, mainly sourced from adverse selection and moral hazard concerns (Kohers and Ang, 2000). Moreover, Datar et al. (2001) illustrate that Common Law countries facilitate, to a great extent, contractual agreements, thus increasing the likelihood of its use. In addition, EC-financing is hypothesized to be implemented by acquirers expecting high value creation from the acquisition which leads to the need to capture the acquirer’s growth opportunities as measured by its market-to-book ratio (Rau and Vermaelen, 1998). Furthermore, as an EC is more likely to be implemented in relatively riskier deals than single upfront payment methods (Kohers and Ang, 2000; Cain et al., 2011), Fuller et al. (2002) suggest that a deal’s transaction value relative to the acquiring firm’s market value prior to the deal’s announcement constitutes an adequate measure of the degree of riskiness of the deal. In addition we also include factors known to influence crossborder takeover activity. These consist of the target country’s level of economic development, the capital controls in place in the target country, the corporate tax rate that is in effect in the target country and the relative strength of the acquiring firm’s currency. Lastly, this study also utilizes certain key financial ratios of the acquiring firm, as further determinants of the decision to engage in an EC-financed deal. They consist of the acquiring firm’s cash ratio (total cash and cash equivalents over total assets), its debt-to-equity ratio (total debt to common equity) and its ratio of net profit over revenue (profit margin). The latter are expected to capture the liquidity, leverage and profitability status of the acquiring firm. Lastly, when matching within NFT_NEW deals we also include the ratio of the acquiring firm’s foreign to total sales. This allows us to capture, to a great extent, the extent to which the acquiring firm’s degree of global diversification affects the probability of EC use, as well as match treated EC-financed deals to NEC-counterfactual deals involving acquirers that are similarly globally diversified.

140