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Darryl Laws

  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...
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Darryl Laws

  Log likelihood. Had I conducted the research I would have chosen to use log likelihood statistic to assess and to depict the  deviance, or -2 log-likelihood (-2LL) statistic . The deviance is basically a measure of how much unexplained variation there is in our logistic regression model the higher the value the less accurate the model. It compares the difference in probability between the predicted outcome and the actual outcome for each case and sums these differences together to provide a measure of the total error in the model. This is similar in purpose to looking at the total of the  residuals  (the  sum of squares ) in linear regression analysis in that it provides us with an indication of how good our model is at predicting the outcome. The -2LL statistic (often called the deviance) is an indicator of how much unexplained information there is after the model has been fitted, with large values of -2LL indicating poorly fitting models. The  deviance...
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Darryl Laws

  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 ...
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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 me...
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Darryl Laws

  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...
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Darryl Laws

  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...
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Darryl Laws

  In concluding their research Malmendier and Tate (2008) investigated the capital market’s perception / reaction to the merger / acquisition decisions made by overconfident CEOs. They used a standard event study methodology to show that outside investors react more negatively to the announcement of a bid if the CEO is overconfident.  Sample. Malmendier and Tate’s (2008) used the sample to test ad validate their propositions. The sample was comprised of 477 large publicly traded U.S. companies between the years 1980 to 1994. To be included in the in the sample required that a company must appear at least four times on one of the lists of largest US companies compiled by Forbes magazine between 1984 to 1994. IPOs were excluded. The core of their data set provides detailed information on the stock ownership and set of option packages, including exercise price, maturity, and number of underlying shares for the CEO of each company in each year. From this data they were able to ...
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