This book proposes complex hierarchical deep architectures (HDA)
for predicting bankruptcy, a topical issue for business and
corporate institutions that in the past has been tackled using
statistical, market-based and machine-intelligence prediction
models. The HDA are formed through fuzzy rough tensor deep staking
networks (FRTDSN) with structured, hierarchical rough Bayesian
(HRB) models. FRTDSN is formalized through TDSN and fuzzy rough
sets, and HRB is formed by incorporating probabilistic rough sets
in structured hierarchical Bayesian model. Then FRTDSN is
integrated with HRB to form the compound FRTDSN-HRB model. HRB
enhances the prediction accuracy of FRTDSN-HRB model. The
experimental datasets are adopted from Korean construction
companies and American and European non-financial companies, and
the research presented focuses on the impact of choice of cut-off
points, sampling procedures and business cycle on the accuracy of
bankruptcy prediction models. The book also highlights the fact
that misclassification can result in erroneous predictions leading
to prohibitive costs to investors and the economy, and shows that
choice of cut-off point and sampling procedures affect rankings of
various models. It also suggests that empirical cut-off points
estimated from training samples result in the lowest
misclassification costs for all the models. The book confirms that
FRTDSN-HRB achieves superior performance compared to other
statistical and soft-computing models. The experimental results are
given in terms of several important statistical parameters
revolving different business cycles and sub-cycles for the datasets
considered and are of immense benefit to researchers working in
this area.
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