A New Approach Of Multiple Merger And Acquisition (M &A) In AR Time Series Model Under Bayesian Framework

Merger and acquisition (M\&As) concepts play a pivotal role in fostering economic development and are extensively examined worldwide across various empirical contexts, notably in the banking sector. The primary objective of this study is to introduce a novel approach termed the multiple-merger autoregressive (MM-AR) model, aimed at providing insights into the effects of mergers on model parameters and behaviour. Initially, we propose a comprehensive estimation framework utilizing posterior parameters within the Bayesian paradigm, incorporating diverse loss functions to enhance robustness. The uniqueness of this model is that it will also work for the situation when multiple series get merged at various time points in the same observed series. Bayesian estimation approach is used to record the results of the MM-AR model parameters in terms of MSE, AB, and AE and get good results. Under Bayesian estimation, SELF performs better than the other estimators for most of the parameters. Subsequently, we compute the Bayes factor to quantify the impact of merged series on the overall model dynamics. To further elucidate the efficacy of the proposed model, we conduct both simulation-based analyses and real-world applications focusing on the Indian banking sector. Through this research, we aim to offer valuable insights into the implications of M\&A activities. For the purpose of data analysis, we used PCR banking data of ICICI Banks Ltd. for simulation and empirical analysis to verify the models' applicability and purpose.