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portada Predictive Capability of Financial Ratios for Forecasting of Corporate Bankruptcy: Corporate Bankruptcy: Reasons and Recovery Strategies
Type
Physical Book
Author
Publisher
Language
English
Pages
76
Format
Paperback
Dimensions
22.9 x 15.2 x 0.4 cm
Weight
0.11 kg.
ISBN13
9781636480039
Categories

Predictive Capability of Financial Ratios for Forecasting of Corporate Bankruptcy: Corporate Bankruptcy: Reasons and Recovery Strategies

Saiful Islam (Author) · Eliva Press · Paperback

Predictive Capability of Financial Ratios for Forecasting of Corporate Bankruptcy: Corporate Bankruptcy: Reasons and Recovery Strategies - Islam, Saiful

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Synopsis "Predictive Capability of Financial Ratios for Forecasting of Corporate Bankruptcy: Corporate Bankruptcy: Reasons and Recovery Strategies"

Bankruptcy of a business firm is an event which results substantial losses to creditors and stockholders. A model which is capable of predicting an upcoming business failure will serve as a very useful tool to reduce such losses by providing warning to the interested parties. This was the main motivation for Beaver (1966) and Altman (1968) to construct bankruptcy prediction models based on the financial data (Deakin 1972). This research study also initiated with a great interest on this subject to investigate the predictive capability of financial ratios for forecasting of corporate distress and bankruptcy events. This study is expounded on similar previous studies by Altman (1968), Ohlson (1980), Beaver (1966) by examining the effectiveness of financial ratios for predicting of corporate distress. The logistics regression analysis (LRA) statistical method is used to scan the risk factors from the previous financial year data and prediction models are constructed which can reasonably classify the expected bankruptcy group and can reasonably predict the solvency status of a firm. The research has been focused on the USA companies only. A set of bankrupted and non-bankrupted company financial data are used for constructing the bankruptcy prediction model and then a second set of bankrupted and non-bankrupted company financial data has been used to test the classification accuracy of the constructed models. The result of this study is consistent with the previous bankruptcy prediction researches outcomes. This study also investigates the time factor implication of bankruptcy prediction models using 5 years financial ratios.

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