An analysis of Satyam case using bankruptcy and fraud detection models

Bankruptcy, which occurs due to inability of a business, to repay its debts and obligations has caught the interest of investors and practitioners alike. Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of event...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Rakesh Yadav [verfasserIn] Ameya Patil [verfasserIn] Rajeev Sengupta [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	satyam inc investment decision peotroski f score beinish m-score z-score

Übergeordnetes Werk:	In: SocioEconomic Challenges - The Academic Research and Publishing UG (i. G.) (AR&P) LLC, 2022, 7(2023), 4, Seite 24-35
Übergeordnetes Werk:	volume:7 ; year:2023 ; number:4 ; pages:24-35

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.61093/sec.7(4).24-35.2023

Katalog-ID:	DOAJ096536624

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title_sort	analysis of satyam case using bankruptcy and fraud detection models
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title_auth	An analysis of Satyam case using bankruptcy and fraud detection models
abstract	Bankruptcy, which occurs due to inability of a business, to repay its debts and obligations has caught the interest of investors and practitioners alike. Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of events such as the global financial crisis of 2008. Early bankruptcy prediction models used traditional statistical techniques via financial ratios. Since then there has been a constant endeavour to develop models with enhanced predictive performance. Satyam Inc. was Indian listed business which went bankrupt in 2007. In this study we apply financial models such as F score, M-score and Z-score to show how common/retail investor who cannot use sophisticated financial tool, can benefit from these simple tools and make good investment decisions. Our research adds to the discussion regarding the capability of bankruptcy prediction models. We derive our findings using the data for Satyam Inc., one of the biggest corporate scandalin India. Before the scam, Beinish M-score acted as more efficient predictor of bankruptcy and fraud than Altman Z-score and Peotroski F score. In fact, the usefulness of Z score and F score was average to poor in predicting Satyam’s bankruptcy in advance. This result contradicts outcomes from several researches who had found a great ultility of Z score and F score. From the policy view, the regulator of financial market can protect the financial illiterate investor who makes investment in capital market to take informed investment decision by using the Beinish M-score for making investing decision in the stock of the company. Similarly, these models can be used by banks and financial institutions in case of existing as well as potential corporate borrowers.
abstractGer	Bankruptcy, which occurs due to inability of a business, to repay its debts and obligations has caught the interest of investors and practitioners alike. Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of events such as the global financial crisis of 2008. Early bankruptcy prediction models used traditional statistical techniques via financial ratios. Since then there has been a constant endeavour to develop models with enhanced predictive performance. Satyam Inc. was Indian listed business which went bankrupt in 2007. In this study we apply financial models such as F score, M-score and Z-score to show how common/retail investor who cannot use sophisticated financial tool, can benefit from these simple tools and make good investment decisions. Our research adds to the discussion regarding the capability of bankruptcy prediction models. We derive our findings using the data for Satyam Inc., one of the biggest corporate scandalin India. Before the scam, Beinish M-score acted as more efficient predictor of bankruptcy and fraud than Altman Z-score and Peotroski F score. In fact, the usefulness of Z score and F score was average to poor in predicting Satyam’s bankruptcy in advance. This result contradicts outcomes from several researches who had found a great ultility of Z score and F score. From the policy view, the regulator of financial market can protect the financial illiterate investor who makes investment in capital market to take informed investment decision by using the Beinish M-score for making investing decision in the stock of the company. Similarly, these models can be used by banks and financial institutions in case of existing as well as potential corporate borrowers.
abstract_unstemmed	Bankruptcy, which occurs due to inability of a business, to repay its debts and obligations has caught the interest of investors and practitioners alike. Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of events such as the global financial crisis of 2008. Early bankruptcy prediction models used traditional statistical techniques via financial ratios. Since then there has been a constant endeavour to develop models with enhanced predictive performance. Satyam Inc. was Indian listed business which went bankrupt in 2007. In this study we apply financial models such as F score, M-score and Z-score to show how common/retail investor who cannot use sophisticated financial tool, can benefit from these simple tools and make good investment decisions. Our research adds to the discussion regarding the capability of bankruptcy prediction models. We derive our findings using the data for Satyam Inc., one of the biggest corporate scandalin India. Before the scam, Beinish M-score acted as more efficient predictor of bankruptcy and fraud than Altman Z-score and Peotroski F score. In fact, the usefulness of Z score and F score was average to poor in predicting Satyam’s bankruptcy in advance. This result contradicts outcomes from several researches who had found a great ultility of Z score and F score. From the policy view, the regulator of financial market can protect the financial illiterate investor who makes investment in capital market to take informed investment decision by using the Beinish M-score for making investing decision in the stock of the company. Similarly, these models can be used by banks and financial institutions in case of existing as well as potential corporate borrowers.
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title_short	An analysis of Satyam case using bankruptcy and fraud detection models
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Predicting bankruptcy prior to the occurrence of event has become crucial in the field of investment and lending, especially in the light of events such as the global financial crisis of 2008. Early bankruptcy prediction models used traditional statistical techniques via financial ratios. Since then there has been a constant endeavour to develop models with enhanced predictive performance. Satyam Inc. was Indian listed business which went bankrupt in 2007. In this study we apply financial models such as F score, M-score and Z-score to show how common/retail investor who cannot use sophisticated financial tool, can benefit from these simple tools and make good investment decisions. Our research adds to the discussion regarding the capability of bankruptcy prediction models. We derive our findings using the data for Satyam Inc., one of the biggest corporate scandalin India. Before the scam, Beinish M-score acted as more efficient predictor of bankruptcy and fraud than Altman Z-score and Peotroski F score. In fact, the usefulness of Z score and F score was average to poor in predicting Satyam’s bankruptcy in advance. This result contradicts outcomes from several researches who had found a great ultility of Z score and F score. From the policy view, the regulator of financial market can protect the financial illiterate investor who makes investment in capital market to take informed investment decision by using the Beinish M-score for making investing decision in the stock of the company. 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An analysis of Satyam case using bankruptcy and fraud detection models

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