Use of modern machine learning techniques to predict the occurrence and outcome of corporate takeover events

The objective of this project is to use machine learning to predict the occurrence of corporate takeovers. The findings show that random forest yields the best predictions out-of-sample based on the area under the curve (AUC) metric. As such, 8 independent variables are considered statistically significant. A time series machine learning approach is also used at the end of the study to predict these events in 2019 based on each company's data from 2010 to 2018. Random forest is still determined as the model with the best out-of-sample performance. A strategy of investing equal amounts across the companies predicted to be takeover targets in 2019 based on the model yields a profit of 7.4%.

Name thesis:degree_name

Master

Department dc:contributor.department

Sloan School of Management

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Geha, Georges.

Advisor dc:contributor.advisor

• David Jean Joseph Thesmar.

• Sloan School of Management. Master of Finance Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/130993

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/130993