Determinants of Wealth Accumulation: Time Series Evidence from Pakistan

Hussain, Abid; Ali, Naveed; Hassan, Izhar Ul

doi:10.31703/ger.2022(vii-i).11

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Altman's model achieved high accuracy levels in predicting the bankruptcy of Singapore retail companies (Muzani & Yuliana, 2021). Z score model was found to accurately predict business bankruptcy for textile companies in Pakistan (Hussain et al, 2014). In case of Greek companies, Altman model performed well at foreseeing failures up to five years in advance (Alexakis, 2008).…”

Section: Review Of Literature and Development Of Hypothesismentioning

confidence: 93%

An analysis of Satyam case using bankruptcy and fraud detection models

Yadav,

Patil,

Sengupta

2023

SEC

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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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Section: Review Of Literature and Development Of Hypothesismentioning

confidence: 93%

An analysis of Satyam case using bankruptcy and fraud detection models

Yadav,

Patil,

Sengupta

2023

SEC

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“…Till now very limited amount of work on default risk assessment has been done for the Pakistan Stock Exchange [5,6] and the analysis has only been done for a small number of individual industries. So, this study has utilized the data of all the listed non-financial companies on the Pakistan Stock Exchange (PSX).…”

Section: Introductionmentioning

confidence: 99%

Firm Specific and Macroeconomic Determinants of Probability of Default: A Case of Pakistani Non-Financial Sector

Hamid¹,

Siddiqui²

2023

IJSDP

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This study extends the literature on the capital structure by examining the effect of firm specific and macroeconomic factors on probability of default using 2385 firm year observations of Non-financial firms listed on Pakistan Stock Exchange (PSX) for the period 1998 to 2021. This is the first study that used a large dataset to analyze the default risk of Pakistani listed non-financial companies. This study follows Bharat and Shumway (2008) methodology to calculate expected default probability, which is a simplified version of the Merton (1974) structural default model. Fixed effect model has been used for data analysis. The empirical results of firm specific variables show that growth, operating cash flow ratio, liquidity, and performance is negatively related with the probability of default while leverage and tangibility of assets are positively related with the probability of default. Size of the company has no relationship with the probability of default. Macroeconomic variables economic conditions measured by GDP growth rate and index return have negative while short term interest rate have positive impact on probability of default. This study may be beneficial to the managers of non-financial companies since it may help them become more aware of the consequences of default risk and may also help them build effective policies linked to managing default risk. The board of directors of non-financial companies can extract valuable information from this study which is required to conduct control measures related to default risk management. The study excluded financial firm because those have different capital structure as compared to non-financial firms. Further studies can also investigate the effect of firm-specific factors and macroeconomic factors in different sectors to check is there any difference in results on sector basis.

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Determinants of Wealth Accumulation: Time Series Evidence from Pakistan

Cited by 2 publications

References 14 publications

An analysis of Satyam case using bankruptcy and fraud detection models

An analysis of Satyam case using bankruptcy and fraud detection models

Firm Specific and Macroeconomic Determinants of Probability of Default: A Case of Pakistani Non-Financial Sector

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