Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/387374
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DC FieldValueLanguage
dc.coverage.spatialCommerce
dc.date.accessioned2022-06-20T04:37:33Z-
dc.date.available2022-06-20T04:37:33Z-
dc.identifier.urihttp://hdl.handle.net/10603/387374-
dc.description.abstractThe banking industry plays a major role in the economic development of a country. An efficient and vibrant banking system is the backbone of the financial sector. The sustainability and growth of banking sector is important for the development of financial sector as well as economic growth. After the liberalization of the Indian economy, the Government of India has announced prudential reform measures based on the recommendations of the Narasimhan Committee to construct the banking industry to become viable and competitively strong. Any major problem in the banking sector ultimately affects the functioning of the economy.The major issue currently faced by the scheduled commercial banks is growingNon-Performing Assets (NPAs). The higher level of NPA implies that the banks have a large number of credit defaulters that ultimately disturb their profitability, liquidity, return on investment and cash flow of the banks. Non-performing assets is an indicator to judge the soundness and financial position of the banking system.Financial stability continues to be the major challenge to the banks. Hence, it becomes important to measure the financial soundness of the banks in order to judge their respective position. The present study covered a period of 15 years, i.e., from 2003-2004 to 2017-2018. The required data was primarily collected from secondary source and the experts opinion was gathered to assign weights to criterion (ratios). Accordingly, a sample of 44 scheduled commercial banks wasselected to explore the financial performance of banks and level of non-performing assets and its recovery through various channels. The study employed multi-criterion decision making approach (VIKOR) to rank the banks and path analysis to know the linkage between the various criterion variables. In addition, 5 non-surviving banks were selected based on the availability of data. The study used survival analysis to predict the financial distress condition of banks.
dc.format.extent211 p.
dc.languageEnglish
dc.relation104
dc.rightsuniversity
dc.titlePrediction of Financial Distress of Scheduled Commercial Banks in India An Application of Survival Analysis
dc.title.alternative
dc.creator.researcherSangamithra S
dc.subject.keywordSocial Sciences
dc.subject.keywordSocial Sciences General
dc.subject.keywordCommerce
dc.description.note
dc.contributor.guideSanthiyavalli G
dc.publisher.placeCoimbatore
dc.publisher.universityAvinashilingam Institute for Home Science and Higher Education for Women
dc.publisher.institutionDepartment of Commerce
dc.date.registered2017
dc.date.completed2021
dc.date.awarded2022
dc.format.dimensions210 mm x 290 mm
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Commerce

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01_title.pdfAttached File101.37 kBAdobe PDFView/Open
02_certificate.pdf263.86 kBAdobe PDFView/Open
03_acknowledgement.pdf13.11 kBAdobe PDFView/Open
04_contents.pdf116.42 kBAdobe PDFView/Open
05_list of tables, figures and abbreviations.pdf129.61 kBAdobe PDFView/Open
06_chapter 1.pdf711.44 kBAdobe PDFView/Open
07_chapter 2.pdf512.09 kBAdobe PDFView/Open
08_chapter 3.pdf783.75 kBAdobe PDFView/Open
09_chapter 4.pdf2.71 MBAdobe PDFView/Open
10_chapter 5.pdf757.11 kBAdobe PDFView/Open
11_bibliography.pdf572.27 kBAdobe PDFView/Open
12_appendices.pdf387.02 kBAdobe PDFView/Open
80_recommendation.pdf353 kBAdobe PDFView/Open


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