Predicting Takeover Success Using Machine Learning Techniques
<span style="font-family: Times New Roman; font-size: small;"> </span><p style="margin: 0in 0.5in 0pt; text-align: justify; mso-pagination: none; mso-layout-grid-align: none;" class="MsoNormal"><span style="color: black; font-size: 10pt; mso-themecolor: text1;"><span style="font-family: Times New Roman;">A takeover success prediction model aims at predicting the probability that a takeover attempt will succeed by using publicly available information at the time of the announcement.<span style="mso-spacerun: yes;"> </span>We perform a thorough study using machine learning techniques to predict takeover success.<span style="mso-spacerun: yes;"> </span>Specifically, we model takeover success prediction as a binary classification problem, which has been widely studied in the machine learning community.<span style="mso-spacerun: yes;"> </span>Motivated by the recent advance in machine learning, we empirically evaluate and analyze many state-of-the-art classifiers, including logistic regression, artificial neural network, support vector machines with different kernels, decision trees, random forest, and Adaboost.<span style="mso-spacerun: yes;"> </span>The experiments validate the effectiveness of applying machine learning in takeover success prediction, and we found that the support vector machine with linear kernel and the Adaboost with stump weak classifiers perform the best for the task.<span style="mso-spacerun: yes;"> </span>The result is consistent with the general observations of these two approaches.</span></span></p><span style="font-family: Times New Roman; font-size: small;"> </span>