0
$\begingroup$

I am planning a research project on estimating the probability of corporate takeovers.
I think that different variables could be indicators to predict takeover bids.
For example, price increases in the days prior to the offer, increased trading volume, takeover rumors in stock market magazines, or business ratios.
From a methodological point of view, I think that machine learning (ML) techniques are very promising with regard to estimating the probability of a takeover.
However, I am not very familiar with this field. Can anyone recommend techniques that can be used to model the probability? Perhaps someone has estimated a probability model with an ML technique in a similar area and can share their experience with me.

I would be very happy to receive feedback.

Many thanks in advance!

$\endgroup$

2 Answers 2

2
$\begingroup$

Your best bet would be to look into a logit-link generalized linear model (GLM) -- what some call logistic regression. That would allow you to put various variables into the model and see how each affects the log-odds of a takeover; and, you can test hypotheses about how significant a given variable is.

Some people might suggest something more complicated; others might claim that logistic regression GLMs are machine learning. Nonsense. Something more complicated will obscure which variables are more vs less informative. As for ML claiming GLMs: GLMs are solid statistical methods that has worked well for decades. Even if you were to do one of the fancier ML techniques, this would be the baseline to compare against.

If you need a reference for this type of model, either Dobson's book or McCullagh and Nelder are the best resources.

$\endgroup$
0
$\begingroup$

"I am planning a research project on estimating the probability of corporate takeovers"

This is not a new idea. There have been at least four sellside papers on this topic between ~2006 and 2017 (those are the ones I'm aware of). At least two of those use ML. You might want to try to get hold of them to avoid reinventing the wheel.

EDIT: papers are

  • DB, Systematic M&A Arbitrage, 2015
  • SocGen, Training your computer to find potential M&A candidates, 2010
  • Wolfe Research, MACHINE LEARNING TAKEOVERS, 2017
  • MS, Introducing ALERT-E: Our European M&A Likelihood Model, 2017

There was also a JPM paper on this topic from, IIRC, 2006 or 2007.

$\endgroup$
2
  • $\begingroup$ Yes this is true. But, can you name this two articles? $\endgroup$
    – TobKel
    May 9, 2021 at 8:50
  • $\begingroup$ Amended my original answer, see above. $\endgroup$
    – user42108
    May 10, 2021 at 17:32

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.