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Bankruptcy prediction through soft c...

Chaudhuri, Arindam.

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Bankruptcy prediction through soft computing based deep learning technique

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bankruptcy prediction through soft computing based deep learning technique/ by Arindam Chaudhuri, Soumya K Ghosh.
作者:	Chaudhuri, Arindam.
其他作者:	Ghosh, Soumya K.
出版者:	Singapore :Springer Singapore : : 2017.,
面頁冊數:	xvii, 102 p. :ill., digital ;24 cm.
內容註:	Introduction -- Need of this Research -- Literature Review -- Bankruptcy Prediction Methodology -- Need for Risk Classification -- Experimental Framework: Bankruptcy Prediction using Soft Computing based Deep Learning Technique -- Datasets Used -- Experimental Results -- Conclusion.
Contained By:	Springer eBooks
標題:	Bankruptcy - Forecasting -
電子資源:	http://dx.doi.org/10.1007/978-981-10-6683-2
ISBN:	9789811066832

Bankruptcy prediction through soft computing based deep learning technique
Chaudhuri, Arindam.

Bankruptcy prediction through soft computing based deep learning technique[electronic resource] /by Arindam Chaudhuri, Soumya K Ghosh. - Singapore :Springer Singapore :2017. - xvii, 102 p. :ill., digital ;24 cm.

Introduction -- Need of this Research -- Literature Review -- Bankruptcy Prediction Methodology -- Need for Risk Classification -- Experimental Framework: Bankruptcy Prediction using Soft Computing based Deep Learning Technique -- Datasets Used -- Experimental Results -- Conclusion.

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.

ISBN: 9789811066832

Standard No.: 10.1007/978-981-10-6683-2doiSubjects--Topical Terms:

3270735
Bankruptcy
--Forecasting

LC Class. No.: HG3761

Dewey Class. No.: 332.75

Bankruptcy prediction through soft computing based deep learning technique
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520 $a 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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