Darryl Laws

 Translation…the manager’s perceived valuation of the merged company minus what he must give up to target shareholders minus the perceived loss due to dilution must exceed his perceived value of A without the merger. Their model denotes the perceived additional merger synergies as be ∈ R++,12. Hence, they decompose 

bV (c) into (2) bV (c) = bVA + VT + e + be – c

Acquisition Decision of a Rational CEO. In comparison to the overconfident CEO Malmendier and Tate (2008) compare him / her to the takeover decision of a single rational CEO. They start with the assumption that the acquiror has all bargaining power and  must pay VT for the target. If he offers an amount c < VT of cash financing (or other non-diluting assets), target shareholders demand a share s of the merged company such that sV (c) = VT − c. Since the CEO acts in the interest of current shareholders, he chooses to conduct the takeover if and only if V (c) −(VT −c) > VA. Malmendier and Tate denote the merger synergies as e ∈ R , and decompose V (c) into V (c) = VA + VT + e − c

Thus, the manager decides to acquire whenever e > 0. The rational CEO makes the best acquisition decision which is independent of c. In this instance, I absolutely do NOT concur with their assumption that there is no extra cost of raising external capital to finance the merger / acquisition in an efficient market condition. Credit and risk premiums are reflective of the balance sheet condition of the acquiror and the credit risk  of the combined companies post acquisition and the company’s ability to debt service. The authors state that in this circumstance the CEO is indifferent to the use of cash on hand, equity (stock), or a combination of the two. This is NOT the real world. The majority of experienced rational CEOs know that post-merger that they are going to undergo a 12-24 months integration period in which they are going to need more working capital to work through their culling and integration. 

Goodness of fit. In their primary model Malmendier and Tate (2008) use a binary logistic regression methodology to predict categorical outcomes from categorical and continuous predictors when predicting which of the two categories a CEO was likely to belong to; overconfident CEOs (aggressive) verses rational CEOs (Field, 2018, pg. 643). The logistic regression or logit model method is appropriate as it is often used to model dichotomous outcome variables. In the logit model the log odds of the outcome are modeled as a linear combination of the predictor variables. 

Darryl Laws