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Going Concern Prediction Models of Liquidated Non-Financial Firms of Pakistan.

Thesis Info

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Author

Javaid, Muhammad Irfan

Program

PhD

Institute

Shaheed Zulfikar Ali Bhutto Institute of Science and Technology

City

Karachi

Province

Sindh

Country

Pakistan

Thesis Completing Year

2019

Thesis Completion Status

Completed

Subject

Economics

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/10910/1/M%20Irfan%20Javaid_Fin_2019_SZABIST_PRR.docx

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676724737062

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The main focus of this study is whether the original version of the prediction models are the best proxy for evaluating the firm’s status as going concern that are operated in creditor-oriented regime. This study is much more precise for the selection of firm as an analysis sample to formulate the prediction model for the firm that are operated in the creditor-oriented regime. The selection is based on such type of firms, whose: a) continuous financial years net losses, b) the external and independent auditors issue the non-going concern opinion of the audited client, and c) all the operations of firms are liquidated. The analysis sample consist of one hundred and twenty-four (124) non-financial firms. In first stage, the validity of the original version of the existing firm’s bankruptcy prediction models, like Altman (1968) and Rashid and Abbas (2011); the original version of the existing going concern prediction models, like Koh and Killough (1990), and Koh (1991); and the original version of the existing liquidation prediction models, like Kuruppur, Laswad and Oyelere (2003) are examined. In second stage these aforesaid models are re-estimated (or updated) and the revised prediction models are formulated for the firms that are operated in creditor regime. In third stage, this study formulated the prediction models by using the MDA and LR techniques to deal with the issues faced by the auditor for evaluating the firm’s status as going concern. The type I error and type II error of the formulated prediction models for evaluating the firm’s status as going concern is less than the type I error and type II error of the existing firm’s prediction models, re-estimated (or updated) version of the existing firm’s prediction models and new prediction models based on the pool of variables. The proposed prediction models are considered as the best alternative for evaluating the firm’s status as going concern. The finding based on five (5) discriminatory ratios that are the part of statement of profit and loss, and the statement of financial positions, the newly formulated prediction models, consist of accounting ratios, is not only a useful tool for accessing the firm’s status as going concern, but also a supportive tool for accessing the firm’s financial fraud risk, for the external and independent auditors in order to construct their opinion for a firm that is operated in the creditor-oriented regime. Implication that emerge from these results has developed the instrument which will be highly recognized by the accounting and auditing practitioners like the auditors of developed economies. Therefore, the regulatory authorities in Pakistan, like SECP, SBP and PSX; and the audit practitioner institutes, like ICAP, and ICMAP, to use these formulated the prediction models for evaluating the firm’s status as going concern that are operated in creditor-oriented regime. The proposed models would also be helpful in monitoring and assessing the financial health of the firms as well.
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نیک عمل تے مان نہ کر

نیک عمل تے مان نہ کر
بدیاں ول دھیان نہ کر
منہ دھروئی گلاں کر
پچھوں پرت گمان نہ کر
پہلے کر تفتیش جناب
ملزم نوں چالان نہ کر
ایہہ تے ہین پرائے ساہ
منگی شے تے مان نہ کر
بریاں نال نہ یاری رکھ
چوراں نوں مہمان نہ کر
سوہنے دا میں امتی ہاں
میرا تے میزان نہ کر

سپین اور ہندوستان میں مسلمانوں کے سیاسی زوال کے مشترک اسباب اور اسلامی تعلیمات

The Muslims ruled over Spain and India for many years. They left over behind themselves many signs of art, architecture, culture and the customs of life. The Muslims both in regions set a remarkable art of living which can still be visualized. It reflects a picture of their high potential and advancement but due to some reasonable causes their journey upwards stopped and the other powers dominated them. In this article the various causes of political decline have been analyzed in Islamic perspective.

Comparison Between the Bayesian and Frequentist Estimators: Univariate Generalized Autoregressive Conditional Heteroskedasticity Garch Model

The estimates of the Maximum Likelihood estimation method are the estimates of the global maximum likelihood function, by definition. However, the present study showed empirically that the likelihood function of the GARCH model is multimodal. Due to the presence of multimodality in the likelihood function leads to a difference in estimates at local and global maxima, and hence, Maximum Likelihood estimation methods can have unstable performance in such situations. Therefore, it will face the problem in inference and prediction, due to the difference in estimates at local and global maxima(s). Two estimation methods are chosen from the Frequentist and the Bayesian approach, respectively, to measure the significance of the difference in estimated parameters due to the presence of multimodality in the likelihood function. Besides, to calculate the level of difference, a standard method of Monte Carlo simulation method is used. The surface plot is constructed by changing the value of the Monte Carlo simulation method to evaluate their performance along the whole surface. these surfaces are then compared within each approach. Subsequently, the preferable algorithms are compared across the Bayesian and Frequentist approaches. For comparison, the present study has calculated bias and variance around the true data generating process. Empirically it is found that in case of Frequentist approach Differential Evolution (DE) algorithm is preferable estimation method for GARCH type models, as compared to Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Because there is multimodality in the likelihood function of the GARCH model, and BFGS uses a single starting value to search maximum point in the likelihood function, and often this single starting value traps into local maxima. Therefore, the estimated parameter at the local and global maxima vary, and hence, inferences and predictions. Conversely, DE uses multiple starting values with multiple chains, due to which it automatically avoid local maxima and converges to global maxima. In the case of the Bayesian approach, Robust Adaptive Metropolis (RAM) is a preferable estimation for GARCH type models as compared to Metropolis Hasting (MH). Because RAM is based on the strategy of adaptive mechanism, i.e., the Markov Chain of the RAM move to the next point, after taking information from the previous point, and finally converge to some particular value of the estimate. While MH use chain of independent nature, i.e., it does not take information while moving from one point to another point in the Markov Chain. After confirming the best estimator from frequentist and the Bayesian approach, this study compared these approaches with each other. Empirically, it is found that the Bayesian approach (RAM) is the preferable estimation method than the Frequentist approach (DE) because the level of bias and variance around the true parameter for RAM is lower than DE. Pakistan Stock Exchange (PSX) is used as a real-world application. Empirically it is found that the Bayesian approach is preferable estimation method than the frequentist approach. Reasons are followed; first, in the frequentist approach estimated parameters are the point estimates, while in the case of the Bayesian approach, the complete distribution of the estimated parameter is obtained at the low cost of simulation. Second, the distribution of the point estimate is hypothetically assumed to be normal, while in case of Bayesian approach it is not valid, i.e., the distribution of the estimates could be skewed in either direction. Therefore, the frequentist approach either over or underestimate the true value of the parameter. Finally, the standard error of the estimates which are obtained through the DE algorithm is more precise as compared to the estimates of BFGS. Therefore, the forecasting based on DE is more accurate about risk and return.