| dc.description.abstract | Mergers and acquisitions are the means by which enterprises can achieve sustainable operation. However, little research has been done on predicting the success of M&A deals before. Therefore, if the company is going to conduct an M&A transaction, it can have a predictive model to predict whether it will be successful after the M&A transaction. Not only can the company′s managers provide assistance in M&A decisions, but it can also allow investors to make more informed investment decisions.
This paper combines text and numerical features to predict the success or failure of mergers and acquisitions. Extract text features from the management discussion and analysis (MD&A) in the 10-K file, construct the text vector through Hierarchical Attention Network, and use the MD&A time change calculated by the company′s MD&A every three years, and combine 15 financial indicators are used as the input factors of the model, and use Bayesian neural network to train model.
This study aims to predict the success of mergers and acquisitions. However, the performance of the experimental results is not as expected, which may come from internal factors or external factors, including financial conditions, merger strategies and other reasons.
Therefore, only using financial indicators or adding text data to forecast may cause inaccurate forecast results. The factors affecting mergers and acquisitions should be considered more comprehensively, and through feature selection, only the features that have an impact on predicting the success of mergers and acquisitions should be extracted for training. And before making a final decision, comprehensively consider the results of various models, professional knowledge in related fields, and market conditions and other information. | en_US |