Final Report Peer Review - Siyao Gu (sg2238) #10
Description
By: Siyao Gu, sg2238
The project aims at predicting the outcome of pending mergers in order to capture the opportunity of investing in merger arbitrage. The data came from SDC Platinum, Fama French Database, Compustat and OptionMetrics, ranging from 1990 to 2016. The group made use of this 8963*73 feature matrix by mainly focusing on the “Status” column as dependent variables for binary classification problem. They first tried logistic regression with L1 penalty and eliminate some features by Lasso. They then implemented simple decision tree, random forest and nearest neighbor, while the overall result turned out to be disappointing. However, they believed that sentiment analysis could potentially improve the model and therefore, the topic would be worthy of further research.
Three things I like about the report:
• The group had a deep sense of what was going on in terms of the topic. To predict the outcome of the merger, they considered features in four categories: valuation, deal characteristic, volatility variables and time series variables, which represented both of the nature, volatility and future move of the deals. Also, compared to other groups, they made use of more than 1 databases for data collection. And that significantly decreased the possibility for underfiting issue in their models.
• They made clever approaches to deal with NA values in the data. The reality that 1600 out of 8963 data points included NA values definitely posed a challenge to the group, while they tried 3 useful ways to effectively fill the NA values instead of deleting all of them. And their final decision of filling NAs with 0 to avoid forward-looking issue did financially make sense.
• They closely followed the principal of avoiding forward-looking bias throughout the whole project and that was truly meaningful. And I particularly like their decision when splitting the data. A simple way to separate the data into training and test data would be randomly splitting all the data, while they thought one step forward, considering the nature of their dataset and splitting the data based on timeline to eliminate look-ahead bias. The group did not find a solid model with an extremely high prediction accuracy in the end, while the details revealed the reliability of their research and how they cared about the topic.
Three things that may need improvement:
• The benchmark the group set was of debt. They mentioned in the report that the original success rate of 83.6% was the benchmark they used, which was significantly high and thus, hard to outperform. I get two questions for this. Firstly, I did not understand how the benchmark was generated even after checking the group’s proposal and midterm report. Secondly, given the fact that I carelessly missed the process where the benchmark was generated, I still believe a false-positive rate or a false-negative rate would be a better benchmark since the problem itself, was a classification problem.
• There could be more intuition written in the Model Selection and Analysis section. The group tried 4 models, while I did not get the connection or the intuition behind the 4 models. For instance, the result of logistic regression was not satisfying as mentioned, then what was the idea of using decision trees afterwards, and why the group used nearest neighbor in the end. The report would be better if the group classified the motivation of transferring from one model to another.
• The last issue is the relationship between the deliveries of the report and the group’s overall topic. Since it is also mentioned in the report that the benchmark was really high and it could be tough to beat the market, the opportunity to capture the opportunity of arbitrage might be even slighter than we thought in a real setting. Therefore, I believe developing trading strategy merely by predicting the success/failure of a merger is not enough and even though there is a model with high prediction accuracy, it may not directly lead to a robust trading strategy. But overall, the group has done an excellent job, and the topic is truly worthy of studying.