Value creation and the probability of success in merger and acquisition transactions

This paper presents and tests a hypothesis that expands existing explanations of value creation in merger and acquisition (M&A) transactions. The main premise is that value creation is determined by not only the target’s pre-acquisition value, as indicated by numerous studies in extant literature, but also by the acquirer’s competency (among other factors) demonstrated by their pre-merger financial ratios. The paper shows that M&A transactions create value in the longer-run and the gain is commensurate with the acquirer’s historical performance and the target’s pre-acquisition value. Further, the paper employs statistical procedures and model-building techniques in order to develop and validate parsimonious Altman-style predictive models. The models reasonably identify successful M&A deals and are statistically significant and consistent with a few existing theories. Specifically, the evidence on liquidity supports internal capital markets hypothesis but does not support the theories of agency problems, while the evidence on financial leverage supports the view that lower debt enhances corporate control.     

Toward a complete definition of relatedness in merger and acquisition transactions

The interrelation between different sources of relatedness in M&A transactions has been largely overlooked in extant literature. This paper offers theoretical and empirical investigation and introduces a few new measures of relatedness. We find that single-dimensional measures of relatedness are complements, not substitutes, of each other, and their impacts on the market’s reaction are additive and interdependent. Specifically, each measure contains unique relatedness information and the market’s perception of, and reaction to, the presence of relatedness in M&A deals is more sophisticated than the extant literature prescribes. The market seems to reward operational and marketing relatedness in small-vicinity mergers and out-of-state mergers. In contrast, related in-state mergers seem to be associated with a significantly negative market reaction. Similarly, technology affiliation induces an additional positive market reaction that is separate from simple industry matching, and the market seems to reward the acquisition of high-technology targets by high-technology acquirers and to penalize the acquisition of high-technology targets by non-high-technology acquirers. Furthermore, we find that horizontally and vertically related mergers are relatively more likely to be completed, while in-state and large-vicinity mergers are less likely to be completed. We also find that when the target is incorporated in a target-friendly state, the merger is less likely to be completed, though state-specific merger laws do not contribute significantly to mergers’ valuation. Taken together, our results indicate that relatedness is a multidimensional metric composed of several interrelated components, and, thus, single-dimensional proxies are not sufficient to capture relatedness accurately and completely.

     

Ethnicity and homophily effects in US M&As

We show that ethnic diversity of CEOs of merging firms has been increasing and report evidence of ethnic homophily effect in M&As transactions. Specifically, M&As perform better when the CEOs of the merging firms share a common ethnic background. In a sample of 444 US mergers completed between 2000 and 2018, we find that ethnic homophily improves the probability of deal completion. Furthermore, we report mild variation of performance across ethnic groups. White CEOs exhibit better postmerger performance while Asian CEOs instigate a better market reaction (announcement return). Subsequent analyses show that White CEOs tend to have longer tenure and longer experience while Asian CEOs tend to be more transparent. We discuss our findings in light of a few extant theories and show that our findings are robust to several additional tests including instrumental variables, Heckman's selection bias correction, and several variations in the model specifications and definitions of key variables.