{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/130993"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/130993","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Use of modern machine learning techniques to predict the occurrence and outcome of corporate takeover events","abstract":"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. 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