Decision-Making in M&A Under Market Mispricing: The Role of Deep Learning Models

Abstract

In the ever-evolving landscape of financial markets, mergers and acquisitions (M&A) play a pivotal role in shaping the corporate ecosystem. However, the presence of market mispricing, driven by various factors such as information asymmetry, behavioral biases, and external shocks, has been a persistent challenge for investors and corporations alike. Understanding the intricate relationship between stock market mispricing and the M&A landscape is crucial for making informed investment decisions and fostering a resilient financial environment. This research explores how stock market mispricing impacts M&A within a fragmented market setting, utilizing deep learning methods to uncover complex patterns and relationships. By analyzing market inefficiencies, the study aims to provide a deeper understanding of how mispricing influences M&A strategies and outcomes. Employing a quantitative descriptive research design, the study gathered valid data through distributed questionnaires, yielding responses from 130 investors and traders, 115 market participants, and 99 regulators and policymakers. The analysis was conducted using the Statistical Package for the Social Sciences (SPSS). Firstly, it establishes the effectiveness of deep learning algorithms in detecting and quantifying stock market mispricing, providing a reliable measure of its extent. The study then explores the differential performance outcomes of companies engaging in M&A during periods of prevalent mispricing compared to those during efficient pricing. The study's novel contribution lies in the introduction of the role of sentiment analysis in deep learning models to incorporate market participants' sentiments, enhancing the accuracy of mispricing detection and its impact on M&A activity. Finally, this research contributes valuable insights into the integration of deep learning techniques in understanding and leveraging stock market mispricing for strategic decision-making in the context of M&A.