AUTHOR=Jiang Tiffany 

TITLE=Using Machine Learning to Analyze Merger Activity

JOURNAL=Frontiers in Applied Mathematics and Statistics

VOLUME=Volume 7 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2021.649501

DOI=10.3389/fams.2021.649501

ISSN=2297-4687

ABSTRACT=An unprecedented amount of access to data, "big data," cloud computing, and innovative technology have increased applications of artificial intelligence in finance and numerous other industries. Machine learning is used in process automation, security, underwriting and credit scoring, algorithmic trading and robo-advisory. In fact, machine learning AI applications are purported to save banks an estimated $447 billion by 2023. Given the advantages that AI brings to finance, we focused on applying supervised machine learning to an investment problem. 10-K SEC filings are routinely used by investors to determine the worth and status of a company. We sought to answer - "Can machine learning analyze more than thousands of companies and spot patterns? Can machine learning automate the process of human analysis in predicting whether a company is fit to merge? Can machine learning spot something that humans cannot?" In the advent of rising antitrust discussion of growing market concentrations and the concern for decrease in competition, we analyzed merger activity using text as a data set. Merger activity has been traditionally hard to predict in the past. In order to verify existing theory and measure harder to observe variables, we look to use a text document and examined a firm’s 10-K SEC filing. To minimize over-fitting, the L2 LASSO regularization technique is used. We came up with a model that has 85 percent accuracy compared to a 35 percent accuracy using the "bag-of-words" method to predict a company’s likelihood of merging from words alone on the same period's test dataset.