| Summary: | Empirical analyses of IPO initial returns are heavily dependent on linear regression models. However, these models can be inefficient due to its sensitivity to outliers which are common in IPO data. In this study, the machine learning method random forest is introduced to deal with the issues the linear regression cannot solve. The random forest is used to predict initial returns of IPOs issued on Borsa Istanbul. The prediction accuracy of the random forest is then tested against methods of robust regression. The prediction results show that random forest has by far outperformed other methods in every category of the comparison. The variable importance measure shows that the IPO proceeds and IPO volume are the most important predictors of IPO initial returns. The results also show that the variables that act as potential proxies for ex-ante uncertainty are more important than variables that are proxies for information asymmetry. Keywords: Random forest, Initial public offerings, Initial returns, Underpricing, Prediction, JEL classification: G12, G30, G39
|
|---|
