Computational Simulation of Hypermarket Merger: Statistical Analysis, AI and Big Data in EV

Objective: This research aims to assess the impact of mergers and acquisitions on the market value (EV) of the ten largest publicly traded hypermarket chains, validating the thesis that such processes create financial, economic, technological, and social value for shareholders and stakeholders. Theoretical Framework: The study is based on the Giftedean neoperspectivist paradigm, which emphasizes adapting analytical methods to advances in AI and data science, and on the postulates of corporate valuation focused on key financial drivers. Method: The research employed the hypothetical-deductive method. Financial data were collected using Python on Google Colab, drawing from sources like Investing.com. A ten-year projection was modeled using statistical and AI tools, including Monte Carlo Simulation (1 million iterations), linear/multiple/logistic regressions, PCA, ANOVA, ARIMA/SARIMA time series, Deep Learning, Machine Learning, Neural Networks, K-Means, and Big Data Analytics. Results and Discussion: The analysis confirmed that ke, ki, WACC, growth rate, and reinvestment rate are the most influential variables on enterprise value post-merger. However, limitations include the time frame and the restricted sample of companies. Research Implications: The study enhances understanding of how mergers affect market value and offers a replicable methodological model for evaluating corporate combinations in different sectors. Originality/Value: The research offers a novel integration of AI, statistics, and Big Data to simulate the effects of mergers on enterprise value, delivering actionable insights into value creation mechanisms in corporate strategies.