MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01960nam a2200217 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20240706150223.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240706b |||||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Uma Maheshwari B, Kavitha D. Sujatha R., Abinaya R and Abhirami B |
| 9 (RLIN) |
39040 |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Bankruptcy prediction models for NSE-Listed firms: A comparative analysis |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Hyderabad |
| Name of publisher, distributor, etc |
IUP Publications |
| Date of publication, distribution, etc |
December 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
5-22 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
According to the Insolvency and Bankruptcy Board of India, the number of companies filing for insolvency witnessed a 30.29% jump to 3,312 in the fourth quarter of 2019. The increasing rate of company failures has prompted efforts to provide better measures to predict bankruptcy well in advance. The objective of the current study is to predict the bankruptcy of firms listed on the National Stock Exchange (NSE) using the data for three years prior to bankruptcy. The data consists of financial variables (categorized into profitability, solvency, liquidity, and activity ratios) and non-financial macroeconomic variables. To achieve this objective, the study uses predictive models such as Altman Z-score, logistic regression, support vector machine (SVM), ensemble methods, artificial neural networks, etc. It also compares the accuracy with performance measures to find the best model for the prediction of financial distress in Indian firms. The findings suggest that logistic regression model has relatively higher (96%) bankruptcy prediction ability, and SVM has the highest model accuracy of 88.54% and demonstrates great ability in predicting healthy companies. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Bankruptcy |
| 9 (RLIN) |
28181 |
|
|---|
| Topical term or geographic name as entry element |
Artificial neural networks |
| 9 (RLIN) |
39041 |
|
|---|
| Topical term or geographic name as entry element |
Prediction models |
| 9 (RLIN) |
39042 |
|
|---|
| Topical term or geographic name as entry element |
Support vector machines |
| 9 (RLIN) |
39043 |
|
|---|
| Topical term or geographic name as entry element |
Comparative studies |
| 9 (RLIN) |
39044 |
| 773 0# - HOST ITEM ENTRY |
|---|
| Host Biblionumber |
30419 |
| Host Itemnumber |
83754 |
| Main entry heading |
MURTHY, E N |
| Place, publisher, and date of publication |
IUP PUBLICATION HYDEARABAD |
| Other item identifier |
55514269 |
| Title |
FINANCIAL RISK MANAGEMENT |
| International Standard Serial Number |
0972-916X |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Journals and Periodicals |
