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Fig. 1: Kernel density distribution plotting of Partially Equivalent (PE) Non-sharia Benchmarks indices against the conventional KSE-100 index benchmark

Figure 1 Kernel density distribution plotting of Partially Equivalent (PE) Non-sharia Benchmarks indices against the conventional KSE-100 index benchmark

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Keywords: IPTFN I-IPTFOWA operator (I-IPTFHA) operator MAGDM problen
Keywords: IPTFN I-IPTFOWA operator (I-IPTFHA) operator MAGDM problen
Table 1: Partially equivalent (PE) non-Sharia benchmarks with appropriate composites io the fundamental composites are T-charges/PIBs, TFCs, Preference offers and so forth a suitable benchmark has been proposed as 6-Month KIBOR rate. Comparable benchmark has additionally been proposed for the Aggressive income funds having the same class risk. On the basis of persistency attribution of the mutual fund performance outcomes over the past few decades, the most distinct criticism is on the presence of error in selecting an appropriate benchmarks out of many (e.g. [3, 22, 24] and so on). This would lead to ask one of the most fundamental questions regarding choice of a benchmark which relates to the particular risk classes of mutual funds. Unlike the past researches on the mutual funds performances based upon the returns statistics which were resulted out after holding the investments for respective years, one of the studies has proposed that one should also examine the performance of individual stocks held by the funds [13, 25]. This has suggested that one ought to likewise inspect the performance of individual stocks held by the funds. The work carried out by Nouman and Shah [26] can be classified as the only research which genuinely tried to incorporate some concept of separate benchmarks as they are used in the KSE-100 index for evaluation of conventional mutual funds and KMI-30 index for evaluation of Sharia complaint funds and held that KMI-30 index was a more appropriate benchmark for evaluation of Sharia complaint funds as compared to KSE-100 index.
Table 1: Partially equivalent (PE) non-Sharia benchmarks with appropriate composites io the fundamental composites are T-charges/PIBs, TFCs, Preference offers and so forth a suitable benchmark has been proposed as 6-Month KIBOR rate. Comparable benchmark has additionally been proposed for the Aggressive income funds having the same class risk. On the basis of persistency attribution of the mutual fund performance outcomes over the past few decades, the most distinct criticism is on the presence of error in selecting an appropriate benchmarks out of many (e.g. [3, 22, 24] and so on). This would lead to ask one of the most fundamental questions regarding choice of a benchmark which relates to the particular risk classes of mutual funds. Unlike the past researches on the mutual funds performances based upon the returns statistics which were resulted out after holding the investments for respective years, one of the studies has proposed that one should also examine the performance of individual stocks held by the funds [13, 25]. This has suggested that one ought to likewise inspect the performance of individual stocks held by the funds. The work carried out by Nouman and Shah [26] can be classified as the only research which genuinely tried to incorporate some concept of separate benchmarks as they are used in the KSE-100 index for evaluation of conventional mutual funds and KMI-30 index for evaluation of Sharia complaint funds and held that KMI-30 index was a more appropriate benchmark for evaluation of Sharia complaint funds as compared to KSE-100 index.
Table 2: Partially Equivalent (PE) Non-Sharia Benchmarks with appropriate composites of the proposed benchmark indices, i.e. SD stands for standard deviation which measures the dispersion of returns from the respective calculated mean values.
Table 2: Partially Equivalent (PE) Non-Sharia Benchmarks with appropriate composites of the proposed benchmark indices, i.e. SD stands for standard deviation which measures the dispersion of returns from the respective calculated mean values.
Table 3: Descriptive statistics of monthly returns of Benchmark indices
Table 3: Descriptive statistics of monthly returns of Benchmark indices
Table 4: Descriptive statistics of monthly returns of sharia benchmark indices stands for standard deviation which measures the dispersion of returns from the respective mean. Kernel Density Estimation was used in order to estimate the probability density function of respective benchmark indices of non-sharia based funds given in Fig. 1.
Table 4: Descriptive statistics of monthly returns of sharia benchmark indices stands for standard deviation which measures the dispersion of returns from the respective mean. Kernel Density Estimation was used in order to estimate the probability density function of respective benchmark indices of non-sharia based funds given in Fig. 1.
Fig. 2: Kernel density distribution plotting of Partially Equivalent (PE) Sharia Benchmarks indices against the conventional KMI-30 index benchmark different picture as compared to the conventional benchmark given in Fig. 2. The results supported the normal distribution pattern in money market benchmark index and generally half of the indices were in support of normal distributed pattern with ability to absorb resulted variations. In addition, further results show that in the data two benchmarks have shown slight variation which can be attributed to small sample size available as KMI 30 index is newly formed benchmark. This further validates our earlier findings in relation to the non-Sharia benchmarks indices as shown in Fig. 2. ANOVA was proposed to justify the difference among conventional and proposed benchmarks. The respective results have been given below on the basis of Non-Sharia benchmark indices and Sharia benchmark indices.
Fig. 2: Kernel density distribution plotting of Partially Equivalent (PE) Sharia Benchmarks indices against the conventional KMI-30 index benchmark different picture as compared to the conventional benchmark given in Fig. 2. The results supported the normal distribution pattern in money market benchmark index and generally half of the indices were in support of normal distributed pattern with ability to absorb resulted variations. In addition, further results show that in the data two benchmarks have shown slight variation which can be attributed to small sample size available as KMI 30 index is newly formed benchmark. This further validates our earlier findings in relation to the non-Sharia benchmarks indices as shown in Fig. 2. ANOVA was proposed to justify the difference among conventional and proposed benchmarks. The respective results have been given below on the basis of Non-Sharia benchmark indices and Sharia benchmark indices.
Table 5: Group unit root test statistics of non-sharia proposed benchmarks of respective riskier classes of mutual funds in Pakistan and KSE-100 ind benchmark: Summary In order to verify the stationary of data proposed bench marks of the conventional mutual funds bench mark of KSE 00 Index and the proposed benchmarks the group unit root test was applied. The test statistics to conclude the stationarity of the data (which are given in Table 5) include Levin, Lin & Chu test, Pesaran and Shin W-stats, Fisher Chi-square. The test statistics show that all of the mutual fund categories showed significant results at 99% confidence interval led us to conclude that the proposed benchmarks are statically valid as there is strong evidence to support our finding details of which are provided in the Table 5.
Table 5: Group unit root test statistics of non-sharia proposed benchmarks of respective riskier classes of mutual funds in Pakistan and KSE-100 ind benchmark: Summary In order to verify the stationary of data proposed bench marks of the conventional mutual funds bench mark of KSE 00 Index and the proposed benchmarks the group unit root test was applied. The test statistics to conclude the stationarity of the data (which are given in Table 5) include Levin, Lin & Chu test, Pesaran and Shin W-stats, Fisher Chi-square. The test statistics show that all of the mutual fund categories showed significant results at 99% confidence interval led us to conclude that the proposed benchmarks are statically valid as there is strong evidence to support our finding details of which are provided in the Table 5.
Table 6: ANOVA test statistics for proposed non-sharia benchmarks against the KSE-100 index benchmark: Summary
Table 6: ANOVA test statistics for proposed non-sharia benchmarks against the KSE-100 index benchmark: Summary
Table 7: ANOVA test statistics for proposed sharia benchmarks against the KMI-30 index benchmark: Summary
Table 7: ANOVA test statistics for proposed sharia benchmarks against the KMI-30 index benchmark: Summary
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