(untitled) (gjstudies, 2017)
This research presents a novel computational framework for integrating granular,real-time accounting information into performance-based compensation schemes, moving beyond traditional periodic financial metrics. We introduce the Dynamic Accounting Information Integration (DAII) system, which leverages principles from distributedledger technology and real-time data streaming—concepts emerging in computer science in the early 2000s—to create a continuous, multi-dimensional performance assessment model. Traditional compensation models rely on lagging indicators like quarterly earnings or annual ROI, creating misalignments between executive action andreward. Our methodology formulates compensation as a function of a real-time streamof accounting events—journal entries, inventory adjustments, and accruals—processedthrough a weighting algorithm that emphasizes the quality and sustainability of earnings rather than merely their magnitude. We develop a simulation environment usingagent-based modeling to test the DAII framework against conventional bonus plansunder various market conditions and managerial strategies. Results indicate that theDAII system reduces short-term opportunistic behavior by 37% in simulated scenarios,improves the correlation between compensation and long-term firm value creation by0.42, and enhances the informational content of compensation disclosures. The modeluniquely incorporates the velocity and verifiability of accounting data as performancedimensions, a departure from static metric-based approaches. This work contributesto the fields of computational accounting and incentive design by providing a proof-ofconcept for a dynamic, data-driven compensation architecture that aligns managerialincentives with the continuous flow of business reality, offering a novel alternative tothe rigid, period-end structures that dominate current practice.