Peer Review by Tiansheng Liu #3
Description
This project involves research on firm merge. They are trying to predict whether a merge will be successful and drive a strategy based on this prediction. They are going to use data about firm financials, stock prices and other relative data from SDC Platinum, CRSP and COMPUSTAT.
Strength:
- This is a great potential application of machine learning in financial industry. If the strategy succeed, it will offer profitable result with low risk. It's also highly related with coursework in this class. Some models and methods talking on class may be used in this project, such like linear regression and classification.
2.This project uses multiple database including SDC Platinum, CRSP and COMPUSTAT. It is certainly good for a research to base on more exhaustive data. But this also bring problems of overfitting and choice between more information which make the project more interesting and complex.
3.This proposal itself is well managed and make things they want to do very clear.
Concerns:
- There have been a whole bunch of investors and professionals studying this event. Maybe the gap between no arbitrage price and fair value has been too small to generate profitable strategy.
- Also, sometimes investment banks earn from event driven event partially because of asymmetric information. They have more resources and information than normal investors. So competing with them is another difficulty in this project.
- Personal factor in merge. Sometimes leaders of both participants in a merge are crucial for the merge. After all, final decision are made by them. However, this factor is difficult to be taken into account in the model.
Looking forward to your progress in project. Hope my suggestion will help you a little bit.