Predicting Status of Pre and Post M&A Deals Using Machine Learning and Deep Learning Techniques

TL;DR

This paper develops a machine learning and deep learning framework to predict the success of M&A deals, improving accuracy over traditional models by integrating various data preprocessing techniques and sentiment analysis.

Contribution

It introduces a comprehensive ML/DL methodology for M&A deal success prediction, including novel data preprocessing and sentiment score integration, outperforming benchmark models.

Findings

ML/DL models outperform logit benchmarks

Sentiment scores have limited impact on performance

Effective handling of class imbalance improves prediction accuracy

Abstract

Risk arbitrage or merger arbitrage is a well-known investment strategy that speculates on the success of M&A deals. Prediction of the deal status in advance is of great importance for risk arbitrageurs. If a deal is mistakenly classified as a completed deal, then enormous cost can be incurred as a result of investing in target company shares. On the contrary, risk arbitrageurs may lose the opportunity of making profit. In this paper, we present an ML and DL based methodology for takeover success prediction problem. We initially apply various ML techniques for data preprocessing such as kNN for data imputation, PCA for lower dimensional representation of numerical variables, MCA for categorical variables, and LSTM autoencoder for sentiment scores. We experiment with different cost functions, different evaluation metrics, and oversampling techniques to address class imbalance in our dataset. We then implement feedforward neural networks to predict the success of the deal status. Our preliminary results indicate that our methodology outperforms the benchmark models such as logit and weighted logit models. We also integrate sentiment scores into our methodology using different model architectures, but our preliminary results show that the performance is not changing much compared to the simple FFNN framework. We will explore different architectures and employ a thorough hyperparameter tuning for sentiment scores as a future work.