Kimberly

Introduction#

Central RQ: CEO overconfidence $\rightarrow$ M&A Performance

The first sentences in your introduction are not true. The empirical result is that usually, there are negative abnormal returns following an M&A announcement, but theoretically, they could be positive, negative or none, dependent on the benefits/costs of merging. This is true in efficient capital markets, and could also be true in inefficient capital markets.

Suggestion: introduce the topic first on a more general level, i.e. the theory, and then argue that empirically, the results are generally negative. Then, introduce the influence of overconfidence on this (negative) premium and finally, introduce your own ideas about the interplay between Covid and overconfidence.

You can still add a short recap of the results (once they’re there) and the remaining thesis structure in the introduction if you want.

Literature Review#

Maybe better to call it a ‘framework’ to fix ideas, because it is really 1 or 2 equations, and models are usually somewhat longer.

  • Use the formula tool in Word/Docs to write formulas.

I think it is “the valuation of a target firm by CEO $i$”, and not “the valuation of firm $i$": the valuation is not firm-specific, but acquiring-CEO specific.

The degree of specificity/granularity seems suitable to me: not too detailed, but also not skipping over anything.

Subsequently, the final offer price p will maximize the willingness-to-pay of the acquiring firm CEO, which produces the following function:

The CEO of the target firm will charge a price $p$, and the expectation of that price is $\mathbb{E}[p] = \dots$.

In Covid-times, the average valuation of a firm by the stock market, $a$ is lower than in non-Covid-times.

  • The structure of the literature review is clear!

Methodology#

  • It is not necessary to describe the event study design in such great detail. (You can just mention the parameters you are using).

  • Baseline model:

$$ BP_i = \alpha + \beta \cdot \text{OverConf}_i + \gamma \cdot \text{CovidDum} + \\
\delta \cdot \text{CovidDum x OverConf} + \epsilon_i $$

  • Control variables

  • Your method talks about how you could measure it. But how do you measure it in fact?

    • Best approach: take many definitions of overconfidence

  • Robustness analyses: heterogeneous effects (discuss which)