Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Status of Pre and Post M&A Deals Using Machine Learning and
  Deep Learning Techniques   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.

37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
28
The categories of this paper are: alg-geom
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th