(untitled) (gjstudies, 2019)
This research presents a novel methodological framework for financial distress predictionthat challenges the prevailing paradigm of complex, multi-source predictive modeling. Whilecontemporary literature increasingly favors models incorporating market data, textual analysis, and macroeconomic indicators, this paper argues for a deliberate and sophisticatedre-engagement with traditional accounting ratios. Our originality stems not from the rediscovery of these ratios, but from a fundamentally new approach to their synthesis andinterpretation. We introduce the concept of ’Ratio Synergy Networks’ (RSNs), a graphtheoretic methodology that models the non-linear, interdependent relationships betweenclassic liquidity, profitability, leverage, and activity ratios. This approach moves beyondtreating ratios as independent variables in a regression model, instead capturing how thepredictive signal of one ratio is contingent upon the values of others, mimicking the holisticassessment performed by expert analysts. Furthermore, we develop a temporal ’Ratio Trajectory Clustering’ (RTC) algorithm that identifies archetypal paths to distress, classifyingfirms not just by their static financial position but by the dynamic deterioration patternof their ratios over a multi-year horizon. Applying this dual-framework to a comprehensivedataset of U.S. public firms from 1990 to 2023, we demonstrate that a model built exclusively on carefully processed traditional ratios can achieve predictive accuracy that matchesor exceeds state-of-the-art hybrid models, while offering superior interpretability and robustness in economic downturns. The findings suggest that the diminishing returns of addingnovel data sources may be greater than previously assumed, and that significant latent predictive power remains untapped within the conventional accounting statement, accessibleonly through more advanced relational and temporal analytics. This research contributesa counter-intuitive yet empirically robust perspective to the financial forecasting literature,advocating for depth over breadth in predictive feature engineering.