Peer Review

[mluop]

The proposed project is to predict whether a merger will succeed or fail, based on data about the two companies (the acquirer and the target). The goal is to know whether to buy or short the target company's stock. The training data will be sourced from SDC Platinum, CRSP, and COMPUSTAT.

What I liked:

1. The problem being tackled is clearly interesting. Our investment company would benefit greatly from having a strong predictive model for merger success. We could make a great deal of money from merger arbitrage.
2. The authors seem well informed about the problem domain. Their summary of the problem was well written and indicates that they have an idea of which features would be especially predictive.
3. There are multiple proposed databases. This can be helpful for sourcing features from many aspects of the companies involved in the merger.

What can improve:

1. The proposed datasets are absolutely massive in width. The process of feature engineering from income statements, balance sheets, etc. may take a long time. Perhaps it's worth limiting the scope of the features being looked at, in order to finish the project in a reasonable time.
2. The proposed datasets are also massive in depth. The number of mergers and companies is huge. It may be worth limiting the types of companies looked at to a smaller scope (e.g. startups only).
3. The first paragraph mentions that in practice, much predictive value comes from reading the press releases and news around a merger. If these features are not something you intend to pursue, it is worth mentioning that in the writeup.