Final Peer Review (wl596)

[WanlinLi]

The project aims to build a model to predict the success/failure of the pending merger, which can be used in merger arbitrage. The data set comes from a variety of sources including SDC Platinum, Fama French Database, Compustat, and OptionMetrics.

Things I like:

1. The group made great efforts on data collection, since their data came from a variety of database. As a result, compared to other groups, they had more features to fit their model.
2. The group did a great job in data preprocessing, like simplifying their problem from a multiclass classification to a binary classification, and removing data points which lacked the Price per Share feature.
3. The group tried many techniques we learnt from class to build models. And they also had great ideas about future improvement.

Things that may need improvement:

1. Filling NAs with 0 may be inaccurate, and may waste the values of the non-NAs entry. I do agree with the group's concern about avoiding look-ahead bias, however the group may try to implement imputation and other methods in a way that can avoid look-ahead bias. For example, to predict a NA in time T, use the mean of the non-NA values from time 0 to T-1.
2. The group has try 4 different models to fit the data, however, I could not quite understand the underlying reasons for choosing the fourth model, i.e. the nearest neighbor classifier. The group may add more explanations about the intuition of choosing this model.
3. The analysis of the model results may be a little short.