Detection of IPO Underpricing by Machine Learning Methods

Student: Kremer Maxim

Educational Programme: Economics (Bachelor)

Year of Graduation: 2020

The research presents an overview of existing machine learning models used to analyze IPO underpricing, as well as an attempt to apply machine learning to detect IPO underpricing on the Russian stock market. This work may be the beginning for more detailed studies of IPO underpricing by various methods, at the moment there are very few such studies. It can also be determined how using the experience of past IPOs contributes to predicting the underpricing of future ones. The model can also be applicable on actual future data. Thus, it will be possible to identify clearly undervalued IPOs and get extra profits based on the results of the first few trading days.

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