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Physics Maths Engineering

Using Machine Learning to Analyze Merger Activity

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Tiffany Jiang

Tiffany Jiang


  Peer Reviewed

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© attribution CC-BY

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423 Views

Added on

2024-10-26

Doi: http://dx.doi.org/10.3389/fams.2021.649501

Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology

Abstract

An unprecedented amount of access to data, “big data (or high dimensional 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–Warren Buffett is frequently cited to read a 10-K a day. 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. We took advantage of the large amount of publicly available filings through the Securities Exchange Commission that give a comprehensive summary of a company, and used text, and an innovative way to analyze a company. 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% accuracy compared to a 35% accuracy using the “bag-of-words” method to predict a company’s likelihood of merging from words alone on the same period’s test data set. These steps are the beginnings of tackling more complicated questions, such as “Which section or topic of words is the most predictive?” and “What is the difference between being acquired and acquiring?” Using product descriptions to characterize mergers further into horizontal and vertical mergers could eventually assist with the causal estimates that are of interest to economists. More importantly, using language and words to categorize companies could be useful in predicting counterfactual scenarios and answering policy questions, and could have different applications ranging from detecting fraud to better trading.

Key Questions about 'Using Machine Learning to Analyze Merger Activity'

The article "Using Machine Learning to Analyze Merger Activity" by Tiffany Jiang, published in Frontiers in Applied Mathematics and Statistics in August 2021, explores the application of machine learning techniques to predict corporate mergers. Source

1. How can machine learning models predict merger activity?

The study investigates the use of machine learning algorithms to analyze financial and textual data from companies' SEC filings, aiming to identify patterns that precede merger announcements. Source

2. What role do financial variables play in merger predictions?

The research examines the significance of various financial metrics, such as retained earnings and current liabilities, in forecasting merger activity, highlighting their predictive power in machine learning models. Source

3. How does natural language processing (NLP) enhance merger prediction models?

The article discusses the integration of NLP techniques to analyze textual data from SEC filings, enabling the extraction of qualitative information that complements quantitative financial data in predicting mergers. Source

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ARTICLE USAGE


Article usage: Oct-2024 to May-2025
Show by month Manuscript Video Summary
2025 May 68 68
2025 April 72 72
2025 March 66 66
2025 February 41 41
2025 January 52 52
2024 December 54 54
2024 November 52 52
2024 October 18 18
Total 423 423
Show by month Manuscript Video Summary
2025 May 68 68
2025 April 72 72
2025 March 66 66
2025 February 41 41
2025 January 52 52
2024 December 54 54
2024 November 52 52
2024 October 18 18
Total 423 423
Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology
copyright icon

© attribution CC-BY

  • 0

rating
423 Views

Added on

2024-10-26

Doi: http://dx.doi.org/10.3389/fams.2021.649501

Related Subjects
Physics
Math
Chemistry
Computer science
Engineering
Earth science
Biology

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