Darryl Laws Infp

  What robustness checks were conducted? Malmendier and Tate discuss the robustness of their results to the changes in the empirical model. Simply stated they focused upon the baseline estimates of binary regression equation provided in regression method use (above). First, they considered; Is the Option in the Money? Their CEO stock option long holder measure of overconfidence is they classify a CEO as overconfident if he ever holds his company stock option(s) until expiration. The less an option is in the money, the less delayed exercise indicates likely overconfidence. As a robustness check of their measure, they require that the option that is held until expiration be at least x% in the money at the beginning of its final year. They vary x between 0 and 100 by increments of 10. As they increase x, the classification as overconfident becomes more restrictive. Concurrently, they hold the definition of rational option exercise behavior constant. (Example: they require that the CE...

  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 me...

  The measurement they use is the coefficient odds ratio (log likelihood statistic). The odds ratio is crucial to the interpretation of logistics regression. It is an indicator of the change in odds resulting from a unit change in the predictor. The resulting statistic is based upon comparing observed frequencies with the predicted model. When the predictor variable is categorical the odds ratio is easier to explain. This is referred to as the likelihood ratio Field, 2018, pg. 614)  There are two very different approaches to answering the goodness of fit question. One is to get a statistic that measures how well you can predict the dependent variable based on the independent variables. These kinds of statistics are referred to as measures of predictive power . Typically, they vary between 0 and 1, with 0 meaning no predictive power whatsoever and 1 meaning perfect predictions. The other approach to evaluating model fit is to compute a goodness-of-fit statistic. Ordinarily the ...

  Regression method used . Malmendier and Tate 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 (independent variable) verses rational CEOs. The logistic regression or logit model method is appropriate as it is often used to model dichotomous outcome variables such as the dependent variable: CEOs holding their stock options to maturity and CEO’s overly acquisitiveness. In the logit model the log odds of the outcome are modeled as a linear combination of the predictor variables. When OLS regression is used with a binary response variable it becomes known as a linear probability model and can be used to describe conditional probabilities. However, the errors (residuals) from the linear probability model violate the homoskedasticity and normality of errors assumptions for OLS regression, resulting in invalid standard erro...

  Their model is binary because there are two kinds of variation that they use to identify the effect of overconfidence on acquisitiveness, cross-sectional and within-company variation. Malmendier and Tate (2008) solve their regression equation using three estimation procedures: 1) a logit regression, makes use of both types of variation, 2) a logit regression with random effects, also makes use of both types of variation but, it explicitly models the effect of the firm, rather than the CEO, on acquisitiveness. I noted that if the estimated effects of overconfidence in the logit equation are due to company effects, they should expect to see a decline in their estimates if they include random effects. The remainder of their solution uses a logit regression with fixed effects. This regression equation makes use only of the second type of variation, the effect of overconfidence on acquisitiveness using only variation between overconfident and rational CEOs within a particular firm. Fo...

  The empirical tests that the Malmendier and Tate (2008) conducted corroborate their results. First, they show that the observed differences in option exercises and merger decisions are not due to inside information. Instead, the hypothetical returns CEOs could have obtained by exercising their options earlier are positive on average. Second, the acquisitions of overconfident managers are distributed uniformly over their tenures suggesting that the effect of overconfidence is a true managerial fixed effect. Third, to bolster their portfolio measure of overconfidence the authors constructed an alternative measure based on how a CEO is characterized in the press. They analyzed the difference in merger activity between CEOs who are portrayed in the business press as confident and optimistic and CEOs who are portrayed instead as reliable, cautious, conservative, practical, frugal, or steady. Controlling for the total number of press mentions, they performed the same empirical analys...