Management Forecast Accuracy and Financial Reporting Credibility Assessment

Abstract

This research introduces a novel, multi-dimensional credibility assessment framework that moves beyond traditional accuracy metrics to evaluate the integrity of management financial forecasts. While prior literature predominantly focuses on forecast
error magnitude and bias, this study posits that credibility—a construct encompassing consistency, transparency, explanatory depth, and contextual alignment—is a more
holistic and predictive indicator of underlying financial reporting quality. We develop
and validate the Credibility Assessment Index (CAI), a composite measure derived
from textual analysis of management discussion and analysis (MDA) sections, historical forecast patterns, and exogenous market data. Our methodology employs a
hybrid approach combining computational linguistics, time-series anomaly detection,
and Bayesian inference to quantify credibility dimensions that are typically assessed
qualitatively. Applying the CAI to a longitudinal dataset of SP 500 firms from 1995
to 2004, we find that credibility scores exhibit significant predictive power for subsequent financial restatements, auditor resignations, and regulatory enforcement actions,
even after controlling for forecast accuracy. Notably, we identify a subset of firms with
high forecast accuracy but low credibility scores, which we term ’precisely misleading’
forecasters; these firms demonstrate a materially higher incidence of future reporting
irregularities. The findings challenge the primacy of accuracy as the sole benchmark for
forecast evaluation and propose a paradigm shift towards credibility-centric assessment
models for investors, regulators, and auditors. This research contributes to the fields of
accounting, corporate finance, and information systems by providing a computationally
rigorous, evidence-based tool for proactive financial reporting surveillance.