Darryl Laws

 Previous literature in corporate finance shows that risk averse CEOs should exercise stock options well before expiration due to the suboptimal concentration of their portfolio in company-specific risk. Malmendier and Tate (2008) classify CEOs as overconfident when they display the opposite behavior. Example: if they hold company stock options until the last year before expiration. This behavior suggests that the CEO is persistently bullish about his company’s future prospects.

Malmendier and Tate (2008) find that overconfident CEOs are more likely to conduct mergers / acquisitions than rational CEOs at any point in time. The higher acquisitiveness of overconfident CEOs on average suggests that overconfidence is an important determinant of merger / acquisition activity. Further, the effect of overconfidence on merger activity comes primarily from an increased likelihood of conducting diversifying acquisitions. Previous literature suggests that diversifying mergers are unlikely to create value in the acquiring firm (Lamont and Polk, 2002).  Thus, it is consistent with current theory that overconfident managers are particularly likely to undertake diversified mergers / acquisitions. Additionally, the authors found that the relationship between overconfidence and the likelihood of doing a merger is strongest when CEOs can avoid equity financing to facilitate the gap in value. More often overconfident CEOs typically prefer to use internal cash or leveraged debt (senior and sub-debt) to finance their mergers / acquisition in lieu of their using their company’s stock as a check book unless their company appears to be overvalued by the capital market.

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 analysis as with the portfolio overconfidence measure. Their results replicated the previous results. Furthermore, they posit that the two measures are highly correlated.

Comments

Post a Comment

Popular posts from this blog

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...
Read more

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...
Read more

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 ...
Read more