Final Peer Review - Meiyi Li (ml2549) #12
Description
The project aims to predict whether a merger would be successful using merger related data such as implied volatility, offer price, whether it's an experienced merger... etc. The motivation for the project is based on the belief that there is a stock price difference ex-merger and post-merger, and if one can predict merger successful, the person can take position in market ex-merger and exit post-merger to gain from the arbitrage.
What I'm impressed about the project is the way they dealt with data. First of all, data was split into before 2008 and post 2008 as training set and post set. They did this to avoid look-ahead bias, which is the bias that future information may conclude some patterns in the past. While they did a good job to recognize and eliminate this potential bias, a new potential problem was introduced which is that merger behavior changes over time, and past pattern may not be able to generalize future pattern. Besides the way they split data, I'm also impressed by that the project finally picked 0 to fill in all NA data. This made me think that by filling missing data with mean/median/PCA may actually introduce noise into the system. I wonder whether the feature with 0 as missing data was dropped by L1 selection. Last, the report is simple and easy to read with good usage of tables and charts.
There are several things that I have questions about or I'm interested in knowing more about. First, I would like to know about the training result and test result of Logistic regression and decision tree with mean, median, PCA as data filling method. Second, I don't think failing to beat the benchmark means we can't take arbitrage. Does your model always succeed in predicting the mergers that brings the most profit? And does your model always help you prevent large potential loss? Instead of focusing on success rate, the project can focus on success rate of mergers that brings large potential profit/loss. By predicting significant events right, the strategy may still be able to out-beat the strategy that just guess 'success' for every merger. Last, for the report, it would be better if you could write out the methodology for each model you used and provide a brief explanation.