2026-09-20T02:50:28Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1309932026-06-06T01:04:44Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David Jean Joseph Thesmar.
Geha, Georges.
Sloan School of Management. Master of Finance Program.
Sloan School of Management
2021-06-17T17:20:34Z
2021-06-17T17:20:34Z
2021
2021
https://hdl.handle.net/1721.1/130993
1256665200
Thesis: M. Fin., Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program, February, 2021
Cataloged from the official PDF version of thesis.
Includes bibliographical references (page 23).
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%.
by Georges Geha.
M.Fin.
M.Fin. Massachusetts Institute of Technology, Sloan School of Management, Master of Finance Program
23 pages
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management. Master of Finance Program.
Use of modern machine learning techniques to predict the occurrence and outcome of corporate takeover events
Thesis
application/pdf
2021-06-17T17:20:34Z
Publication
Master
Sloan
unknown
unknown
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