Predictive Modeling Techniques for Bankruptcy Forecasting Using Accounting Data

Abstract

This research introduces a novel hybrid predictive modeling framework for
bankruptcy forecasting that integrates fractal analysis of financial time series with
ensemble machine learning techniques, specifically tailored to the structural characteristics of accounting data. Traditional bankruptcy prediction models have predominantly relied on static financial ratios and linear discriminant analysis, often
overlooking the dynamic, non-linear interdependencies inherent in corporate financial trajectories. Our methodology diverges fundamentally by applying concepts
from statistical physics and complex systems theory to pre-process accounting variables, extracting multi-scale fractal dimensions and persistence metrics as novel
predictive features. These features capture the long-memory and scaling properties
of a firm’s financial evolution, which we hypothesize are early indicators of systemic
instability preceding formal bankruptcy declarations. We develop a cascaded ensemble model, where a primary layer of specialized classifiers—including a tailored
Recursive Partitioning Tree and a Support Vector Machine with a custom financial
kernel—operates on the fractal-transformed feature space. Their outputs are then
synthesized by a meta-learner that incorporates temporal weighting based on the
recency and volatility of financial statements. Testing on a longitudinal dataset of
U.S. manufacturing firms from 1980 to 2000, our model demonstrates a significant
improvement in early warning capability, achieving a mean lead time of 8 quarters
prior to bankruptcy with a 94.2% accuracy, compared to 6 quarters and 88.7%
for the best-performing traditional Altman Z-score derivative. Furthermore, the
model reveals a previously undocumented ’critical slowing down’ phenomenon in
the fractal dimensions of key liquidity ratios in the 12 quarters preceding failure,
providing a new theoretical lens for corporate financial distress. This work establishes a paradigm shift from ratio-based to pattern-based bankruptcy forecasting,
with implications for risk management, regulatory monitoring, and the development
of early intervention systems.