Accounting Information in Merger and Acquisition Decision Processes (gjstudies, 2026)
This research introduces a novel computational framework that re-conceptualizesthe role of accounting information within merger and acquisition (MA) decision-making.Departing from traditional financial ratio analysis and discounted cash flow models, thisstudy proposes a hybrid methodology that integrates principles from quantum information theory, computational linguistics, and complex adaptive systems to model the MAdecision process as a dynamic, non-linear information processing system. We posit thataccounting data, rather than being static inputs for valuation, function as entangledinformation states that interact with managerial cognition, market sentiment, and organizational memory. Our methodology employs a quantum-inspired probabilistic modelto represent accounting variables as superposition states, whose collapse into definitivevaluation metrics is mediated by decision-theoretic gates analogous to quantum logicoperations. This approach allows for the formal representation of ambiguity, information asymmetry, and the path-dependent evolution of deal rationale. Furthermore, weapply natural language processing techniques to analyze the linguistic patterns in internal MA committee deliberations and public disclosures, establishing a semantic bridgebetween quantitative accounting figures and qualitative strategic narratives. Throughsimulation on a novel dataset comprising both successful and failed MA transactionsfrom 1995-2004, our results demonstrate that the predictive accuracy of deal successand post-merger integration performance significantly increases when accounting information is processed through our proposed entangled-state framework, compared toconventional 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 pathologiesleading to value destruction. This research contributes a fundamentally new theoreticallens for understanding financial information in strategic decisions, with implicationsfor the design of next-generation decision support systems, corporate governance, andaccounting standard setting. The cross-disciplinary fusion of concepts establishes afoundation for a more nuanced, computationally robust analysis of one of corporate strategy’s most critical and complex processes