Financial Distress Prediction Models Using Accounting Based Performance Indicators (gjstudies, 2021)
This research introduces a novel methodological framework for predicting corporate financial distress by integrating traditional accounting-based performance indicators with adynamic, multi-period trajectory analysis, diverging significantly from static, single-periodclassification models prevalent in prior literature. The study posits that the path to distressis characterized by identifiable patterns in accounting ratios over time, rather than isolatedthreshold breaches. We develop a Temporal Distress Signature (TDS) model that analyzesthe sequential behavior and inter-temporal relationships of key financial ratios—includingliquidity, profitability, leverage, and efficiency metrics—across a rolling five-year windowpreceding a distress event. Our methodology employs a hybrid approach combining nonparametric pattern recognition algorithms, adapted from signal processing, with a probabilistic state-transition framework to quantify the likelihood of distress progression. Using auniquely constructed longitudinal dataset of U.S. manufacturing and service firms from 1985to 2004, which includes both publicly distressed firms and a matched sample of survivors,we demonstrate that the TDS model achieves a superior out-of-sample prediction accuracyof 94.7% two years prior to distress, compared to 78.2% for the best-performing traditionalstatic model (Altman’s Z”-score). Furthermore, the model successfully identifies distinct’archetypal distress pathways,’ revealing that firms often follow one of several predictabletrajectories, such as the ’Profitability Erosion’ path or the ’Leverage Spiral’ path, each withits own early-warning signature. This pathway analysis provides managers and stakeholders with not just a binary warning, but a diagnostic assessment of the underlying financialdeterioration mode. The research contributes a fundamentally new, dynamic perspective tofinancial distress prediction, moving the field beyond cross-sectional classification towards amore nuanced understanding of financial decline as a temporal process