Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim
The growing population of Muslims around the world, including Malaysia, has led to an increase in demand for Shariah-compliant financial products, especially Islamic stocks. The Covid-19 outbreak which started to spread starting in early 2020 has caused uncertainty in the stock market especially in...
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my.uitm.ir.795132023-06-16T03:24:14Z https://ir.uitm.edu.my/id/eprint/79513/ Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim Ahmad Mihafizuddin, Ahmad Nazirul Iman Ab Aziz, Fatin Nur Azmina Azmi @ Ibrahim, Nadia Izzati Mathematical statistics. Probabilities The growing population of Muslims around the world, including Malaysia, has led to an increase in demand for Shariah-compliant financial products, especially Islamic stocks. The Covid-19 outbreak which started to spread starting in early 2020 has caused uncertainty in the stock market especially in the healthcare sector. Recently, the healthcare sector industry is growing rapidly due to the drastic increase in various products such as gloves, face masks, medicines and vitamins. Therefore, the main purpose of this study is to rank the performance of 14 Shariah-compliant stocks in the healthcare sector listed in the Bursa Malaysia using various Grey Relational Analysis (GRA) models. The Grey Relational Analysis (GRA) models used in the study involving models from Deng’s (0.5), Deng’s (1.0), Wu’s and Wen’s models. A sample of data of stock prices from 2017 until 2021 are obtained from 14 main companies. The results consistently show that TMCLIFE is the highest performing stock despite utilizing various GRA models with different distinguishing coefficients. In addition, the outputs from the Spearman’s rank correlation indicates that Deng’s (0.5) is the best method to evaluate the performance of the Shariah-compliant healthcare stocks. Practically, the findings of this can help investors in making investment decisions. 2022 Student Project NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/79513/1/79513.pdf Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim. (2022) [Student Project] (Unpublished) |
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Mathematical statistics. Probabilities Ahmad Mihafizuddin, Ahmad Nazirul Iman Ab Aziz, Fatin Nur Azmina Azmi @ Ibrahim, Nadia Izzati Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim |
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The growing population of Muslims around the world, including Malaysia, has led to an increase in demand for Shariah-compliant financial products, especially Islamic stocks. The Covid-19 outbreak which started to spread starting in early 2020 has caused uncertainty in the stock market especially in the healthcare sector. Recently, the healthcare sector industry is growing rapidly due to the drastic increase in various products such as gloves, face masks, medicines and vitamins. Therefore, the main purpose of this study is to rank the performance of 14 Shariah-compliant stocks in the healthcare sector listed in the Bursa Malaysia using various Grey Relational Analysis (GRA) models. The Grey Relational Analysis (GRA) models used in the study involving models from Deng’s (0.5), Deng’s (1.0), Wu’s and Wen’s models. A sample of data of stock prices from 2017 until 2021 are obtained from 14 main companies. The results consistently show that TMCLIFE is the highest performing stock despite utilizing various GRA models with different distinguishing coefficients. In addition, the outputs from the Spearman’s rank correlation indicates that Deng’s (0.5) is the best method to evaluate the performance of the Shariah-compliant healthcare stocks. Practically, the findings of this can help investors in making investment decisions. |
format |
Student Project |
author |
Ahmad Mihafizuddin, Ahmad Nazirul Iman Ab Aziz, Fatin Nur Azmina Azmi @ Ibrahim, Nadia Izzati |
author_facet |
Ahmad Mihafizuddin, Ahmad Nazirul Iman Ab Aziz, Fatin Nur Azmina Azmi @ Ibrahim, Nadia Izzati |
author_sort |
Ahmad Mihafizuddin, Ahmad Nazirul Iman |
title |
Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim |
title_short |
Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim |
title_full |
Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim |
title_fullStr |
Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim |
title_full_unstemmed |
Determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / Ahmad Nazirul Iman Ahmad Mihafizuddin, Fatin Nur Azmina Ab Aziz and Nadia Izzati Azmi @ Ibrahim |
title_sort |
determining the ranking of shariah-compliant stocks in the healthcare sector by using grey relational analysis models / ahmad nazirul iman ahmad mihafizuddin, fatin nur azmina ab aziz and nadia izzati azmi @ ibrahim |
publishDate |
2022 |
url |
https://ir.uitm.edu.my/id/eprint/79513/1/79513.pdf https://ir.uitm.edu.my/id/eprint/79513/ |
_version_ |
1769846660947509248 |