(untitled) (gjstudies, 2025)
This research introduces a novel, multi-dimensional framework for assessingmanagement forecast accuracy that moves beyond traditional point-estimate comparisons to incorporate temporal consistency, uncertainty calibration, and narrativealignment. While existing literature predominantly treats forecast accuracy as aunivariate metric influencing market reactions, this study reconceptualizes accuracy as a dynamic credibility signal embedded within the broader financial reporting ecosystem. We develop and validate the Integrated Forecast Credibility Score(IFCS), a composite measure derived from machine learning analysis of linguisticfeatures in management discussion, historical forecast error patterns, and the structural properties of uncertainty disclosures. Applying this framework to a uniquelongitudinal dataset of 850 publicly traded firms from 2015 to 2023, we demonstratethat forecast accuracy, when measured multidimensionally, exhibits a non-linearand context-dependent relationship with reporting credibility as perceived by sophisticated investors and analysts. Crucially, we find that moderate, well-calibratedinaccuracy in volatile periods can enhance credibility more than robotic precision,challenging the prevailing ’more accurate is always better’ paradigm. The resultsreveal that credibility is built not through infallibility, but through a transparent,consistent, and explainable forecasting process, where deviations are rationally justified and linguistically coherent with the firm’s strategic narrative. This researchcontributes a new theoretical lens for understanding financial communication, offering practical tools for managers to build trust and for regulators to assess thequality of forward-looking disclosures