Accounting Information in Merger and Acquisition Decision Processes

Abstract

This research introduces a novel computational framework that re-conceptualizes
the role of accounting information within merger and acquisition (MA) decision-making.
Departing from traditional financial ratio analysis and discounted cash flow models, this
study proposes a hybrid methodology that integrates principles from quantum information theory, computational linguistics, and complex adaptive systems to model the MA
decision process as a dynamic, non-linear information processing system. We posit that
accounting data, rather than being static inputs for valuation, function as entangled
information states that interact with managerial cognition, market sentiment, and organizational memory. Our methodology employs a quantum-inspired probabilistic model
to represent accounting variables as superposition states, whose collapse into definitive
valuation metrics is mediated by decision-theoretic gates analogous to quantum logic
operations. This approach allows for the formal representation of ambiguity, information asymmetry, and the path-dependent evolution of deal rationale. Furthermore, we
apply natural language processing techniques to analyze the linguistic patterns in internal MA committee deliberations and public disclosures, establishing a semantic bridge
between quantitative accounting figures and qualitative strategic narratives. Through
simulation on a novel dataset comprising both successful and failed MA transactions
from 1995-2004, our results demonstrate that the predictive accuracy of deal success
and post-merger integration performance significantly increases when accounting information is processed through our proposed entangled-state framework, compared to
conventional linear models. The model uniquely identifies specific configurations of accounting information entanglement—particularly involving goodwill, contingent liabilities, and revenue recognition policies—that correlate with decision-making pathologies
leading to value destruction. This research contributes a fundamentally new theoretical
lens for understanding financial information in strategic decisions, with implications
for the design of next-generation decision support systems, corporate governance, and
accounting standard setting. The cross-disciplinary fusion of concepts establishes a
foundation for a more nuanced, computationally robust analysis of one of corporate strategy’s most critical and complex processes