Mergers and acquisitions transactions strategies in diffusion - type financial systems in highly volatile global capital markets with nonlinearities

TL;DR

This paper explores strategies for mergers and acquisitions in volatile financial systems, developing AI-based tools and analyzing nonlinear quasi-periodic oscillations to improve transaction success prediction.

Contribution

It introduces a novel AI-driven software for optimizing M&A strategies and analyzes nonlinear oscillations in transaction data to understand market dynamics.

Findings

M&A transaction numbers exhibit quasi-periodic oscillations.

Polynomial models effectively approximate transaction oscillations.

Absorbing new knowledge is crucial for successful M&A deals.

Abstract

The M and A transactions represent a wide range of unique business optimization opportunities in the corporate transformation deals, which are usually characterized by the high level of total risk. The M and A transactions can be successfully implemented by taking to an account the size of investments, purchase price, direction of transaction, type of transaction, and using the modern comparable transactions analysis and the business valuation techniques in the diffusion type financial systems in the finances. We developed the MicroMA software program with the embedded optimized near-real-time artificial intelligence algorithm to create the winning virtuous M and A strategies, using the financial performance characteristics of the involved firms, and to estimate the probability of the M and A transaction completion success. We believe that the fluctuating dependence of M and A transactions number over the certain time period is quasi periodic. We think that there are many factors, which can generate the quasi periodic oscillations of the M and A transactions number in the time domain, for example: the stock market bubble effects. We performed the research of the nonlinearities in the M and A transactions number quasi-periodic oscillations in Matlab, including the ideal, linear, quadratic, and exponential dependences. We discovered that the average of a sum of random numbers in the M and A transactions time series represents a time series with the quasi periodic systematic oscillations, which can be finely approximated by the polynomial numbers. We think that, in the course of the M and A transaction implementation, the ability by the companies to absorb the newly acquired knowledge and to create the new innovative knowledge bases, is a key predeterminant of the M and A deal completion success as in Switzerland.