Bankruptcy Prediction through Soft Computing based Deep Learning Technique

  • Arindam Chaudhuri
  • Soumya K Ghosh

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 1-3
  3. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 5-6
  4. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 7-11
  5. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 13-17
  6. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 19-20
  7. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 39-45
  8. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 47-95
  9. Arindam Chaudhuri, Soumya K. Ghosh
    Pages 97-97
  10. Back Matter
    Pages 99-102

About this book


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.


Bankruptcy Prediction Deep Learning Soft Computing Mathematical Finance Hierarchical Deep Architectures (HAD) Fuzzy Rough Tensor Deep Stacking Networks (FRTDSN) Hierarchical Rough Bayesian (HRB) Fuzzy Rough Sets FRTDSN-HRB Risk Classification

Authors and affiliations

  • Arindam Chaudhuri
    • 1
  • Soumya K Ghosh
    • 2
  1. 1.Samsung R & D Institute DelhiNoidaIndia
  2. 2.Department of Computer Science and EngineeringIndian Institute of Technology KharagpurKharagpurIndia

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Singapore Pte Ltd. 2017
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-10-6682-5
  • Online ISBN 978-981-10-6683-2
  • Buy this book on publisher's site