Author(s)

Yannis Baker

Affiliation(s)

Rutgers Business School, Newark, USA.

Corresponding Author

Yannis Baker

ABSTRACT

This paper presents a predictive model designed to assess the likelihood of success for announced mergers and acquisitions (M&A) by integrating financial data with natural language processing (NLP) techniques applied to company statements. M&A transactions are critical for corporate growth and strategic realignment; however, a significant percentage — approximately 50-70% — fail to create shareholder value. By leveraging financial performance indicators such as revenue growth and profitability, alongside sentiment analysis of textual data from press releases and earnings calls, the model aims to enhance predictive accuracy. The methodology includes data collection from reputable financial databases and textual sources, followed by rigorous analysis using machine learning algorithms. Initial findings suggest that firms with strong pre-merger financial health and positive sentiment in communications are more likely to achieve successful outcomes. This research contributes to the understanding of M&A success factors, offering practical implications for corporate decision-making and future M&A strategies.

KEYWORDS

Mergers and acquisitions; Predictive modeling; Natural language processing

CITE THIS PAPER

Yannis Baker. Integrating qualitative and quantitative data for predicting merger success. Journal of Trends in Financial and Economics. 2024, 1(1): 43-51. DOI: 10.61784/jtfe3009.

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