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This paper aims to examine the performance of Islamic and conventional stocks listed at the Pakistan Stock Exchange by using both parametric and non-parametric approaches. The motivation is to do risk-return analysis of Islamic stock prices and conventional stock prices.

It uses various measures of performance, e.g. Sharpe ratio, Treynor ratio, Jensen's alpha, beta, generalized auto-regressive conditional heteroskedasticity and stochastic dominance. Using the Karachi Meezan Index-30 (KMI-30) and the Karachi Stock Exchange Index-30 (KSE-30) as proxies for Islamic and conventional stock prices, respectively, it examines the performance of Islamic and conventional stocks. The daily data of KMI-30 and KSE-30, covering period from June 9, 2009 to June 20, 2020 are used.

The results show that the overall KMI-30 outperforms the KSE-30. The returns of the KMI-30 are greater than the KSE-30. However, the risk and volatility of the KMI-30 and KSE-30 are similar. Further, the KMI-30 has higher excess returns per unit of total risk than the KSE-30. But both indexes have similar excess returns per unit of systematic risk. Moreover, the KMI-30 returns have stochastically dominance over the KSE-30 returns. These results reveal that the Islamic index performs better than the conventional index.

The findings provide several practical implications in financial and investment decisions making by investors, managers and policymakers such as strategies for asset allocation and investment. Further, in risk management, it provides guidance for allocating portfolios and managing risk. The investment in Islamic stocks may mitigate potential risk within asset portfolios.

This research is unique in its approach to the analysis of the performance comparison of conventional and Islamic stock by using comprehensive parametric and non-parametric estimation techniques. Such research has not been undertaken in the Pakistan's equity market since.

This paper compares the predictive power of linear econometric and non-linear computational models for forecasting the inflation rate in the European Monetary Union (EMU). Various models of both types are developed using different monetary and real activity indicators. They are compared according to a battery of parametric and non-parametric test statistics to measure their performance in one- and four-step ahead forecasts of quarterly data. Using genetic-neural fuzzy systems we find the computational approach superior to some degree and show how to combine both techniques successfully.

The paper investigates relative efficiency of the banking industry in Bahrain by employing a panel of 31 banks for the years 1998 and 2000. We employ non‐parametric (Data Envelopment Analysis) to examine five efficiency measures, namely, cost, allocative, technical, pure technical and scale efficiency scores. We also investigate the conventional accounting measures of performance, and correlate them with five measures of efficiency to investigate whether higher accounting performance impact the bank cost efficiency. Our results show that, on the average, the banking industry in Bahrain is profitable with average ROE and ROA being 10.36% 1.622% in 1998 while 13.49% and 2.097% in 2000 respectively. The average allocative efficiency (inefficiency) is about 73% (37%), whereas the average technical efficiency (inefficiency) is about 56% (78%). This indicates that the dominant source of inefficiency in Bahrain banks is due to technical inefficiency rather than allocative inefficiency, which is mainly attributed to diseconomies in scale. Overall, average scale efficiency (inefficiency) is about 79% (26%), and average pure technical efficiency (inefficiency) is about 71% (41%), suggesting that the major source of the total technical inefficiency for Bahrain banks is pure technical inefficiency (input related) and not scale inefficiency (output related). The results also indicate that all banks have improved their efficiency levels and experienced some gains in productivity. Finally, regression analysis is used to investigate the determinants of the overall efficiency scores. We find that larger and profitable banks are more likely to operate at a higher level of efficiency. Also, another finding reveals that market power plays an important role in cost and technical efficiencies. Notably, banks with greater contribution from shareholders tend to be more technical efficient

This paper aims to investigate how professionals such as accountants, auditors, senior civil servants and academics perceive the use of audit sampling strategies adopted by professionals to increase detection rates of frauds and corruption within the public sector in Africa. It also examines the respondents’ perceived values regarding the reasons for committing frauds, types of fraud and corruption, as well as the aspects of audit sampling strategies to tackle frauds.

This research uses non-parametric statistics and logistic regression to analyse the respondents’ opinions regarding the state of frauds and corruption in Africa (particularly in Tunisia and non-Tunisia countries), the common factors behind people committing frauds, including the types of frauds and corruption and the respondents’ opinions on the use of audit sampling strategies (non-random and random) to examine the instances of frauds and corruption.

The findings indicate that most respondents prefer to use non-probabilistic audit sampling rather than more robust sampling strategies such as random sampling and systematic random sampling to detect frauds and corruption. In addition, although there are some minor statistical differences between the countries in terms of the respondents’ perceived values on skimming fraud and on the use of audit random sampling to tackle rampant corruption in Africa, the overall findings indicate that opinions do not significantly differ between the respondents from Tunisia and other countries in terms of the types of fraud, the reasons for committing fraud and the auditing sampling strategies used to investigate the frauds.

This research serves as an analytical exploratory study to instigate further audit sampling research to combat rampant fraud and corruption in the public sector in Africa.

There are few or non-existent studies investigating the application of audit sampling strategies in Africa countries, particularly to examine the application of audit random sampling and audit non-random sampling strategies to detect fraudulent activities and corruption. Correspondingly, this research carries strategic implications for accountants and auditors to successfully detect fraudulent activities and corruption in Africa.

The purpose of this paper is to examine the usefulness of a heuristic expert system, to show its applicability to real‐world valuation problems, and to suggest several avenues for statistical testing.

The expert systems follow a traditional sales adjustment grid format, with sufficient data for non‐parametric testing.

The paper finds that, while non‐parametric statistics provide weaker results than traditional (e.g. hedonic regression) modeling, the technique provides a statistically testable model useful in situations with limited data and/or poorly characterized probability functions.

This paper addresses the conundrum faced by real estate valuers on the lack of statistical underpinnings of traditional heuristic models.

This is one of the first empirical studies in the valuation literature exploring statistical characterization of heuristic valuation methods.

Statistical methods are important for meaningful analysis, critique and interpretation of results. The current study aims to investigate the use of statistical methods used in LIS research articles produced by Pakistani authors during 2001–2016.

Content analysis method with both the qualitative and quantitative components was used. LIS articles published by Pakistani authors in national and international journals from 2001 to 2016 were selected. The descriptive and inferential statistics were used to analyze the usage of statistical techniques.

The findings show that use of descriptive statistics remained higher as compared to inferential statistics in the LIS research produced by Pakistani authors. However, a visible growth trend in the use of inferential statistical techniques is found. Males are two times more likely to use inferential statistics as compared to female authors. Articles published in foreign journals and impact factor journals used more inferential statistics as compared to local and nonimpact factor journals. Parametric inferential statistics is more popular among Pakistani authors as compared to nonparametric. Faculty was more inclined toward using parametric statistic. The percentage of collaboration was higher in the papers using parametric statistics. Few articles reported the tests to fulfill the assumptions of parametric and nonparametric statistics.

This study can be used to better understand the trends of statistical techniques used in LIS research and authors' orientation in this regard. It will be helpful for future researchers in the selection of appropriate statistical techniques to be used.

The present study, by using signaling perspective aims to investigate short-term valuation impact of rebranding announcements (with name change) on stock performance of 160 service firms listed on NSE NIFTY-500 over the period of 2000–2019.

An event study methodology is used to estimate the cumulative abnormal returns (CARs) and its statistical significance is tested with both parametric and non-parametric test-statistics. Separate analysis has been conducted for firms with “major vs minor” and “restructuring vs non-restructuring” name change.

Findings of the study suggest that rebranding decisions are negatively associated with abnormal returns around the announcement period indicating strong disapproval of name change event. In addition, investors formed strong adverse opinion for major name change firms as compared to minor name change firms. Further, restructured name change sample document larger negative drift than non-restructured sample.

Findings offer substantial repercussions for shareholders who can make informed judgments about name change as a signal of reinventing brand identity. Managers should announce detailed rationale behind name change decision to market for enhancing corporate reputation.

This study contributes to marketing-finance interface literature and is first to examine market reaction to name change of Indian service firms and moreover, made a distinction between major vs minor and restructured vs non-restructured name change events for these firms.

The purpose of this paper is to investigate and compare the weak‐form efficiency of a set of 24 African continent‐wide stock price indices and those of eight individual African national stock price indices.

Variance‐ratio tests based on ranks and signs were used to examine the weak‐form efficiency of the 32 stock price indices investigated.

On average, it was found that irrespective of the test employed, the returns of all the 24 African continent‐wide stock price indices examined in the study are less non‐normally distributed compared to the eight individual national stock price indices examined. The authors also report evidence of the African continent‐wide stock price indices having significantly better weak‐form informational efficiency than their national counterparts.

The policy implication of this evidence is that the African equity price discovery process can be significantly improved if African stock markets integrate their operations. Economically, this may contribute to improved liquidity and more efficient allocation of capital, which in turn can be expected to have a positive impact on economic growth.

The paper makes two major contributions to the extant literature. First, it offers for the first time a comparative analysis of the informational efficiencies of a sample of national stock price indices as against African continent‐wide stock price indices. Second, there is no prior evidence as to whether African stock markets can improve their informational efficiencies by integrating their operations. The paper fills this gap by demonstrating that the African equity price formation process can be improved if African stock markets integrate their operations.

Internet service providers (ISPs) constitute a highly competitive market, while the unstable market conditions directly affect customer preferences and make ISPs to develop a “mass customization” strategy, individualizing services and approaching every customer in an individual way. However, customization requires an in‐depth analysis of current customer preferences and an evaluation of future behavior. The main objective of the paper is to present a framework for analyzing changes of customer preferences.

The paper presents detailed results of independent customer satisfaction surveys conducted in different time periods in the Greek ISP market. The analyses are based on non‐parametric statistical techniques and the multicriteria satisfaction analysis method, which is a multicriteria preference disaggregation approach.

Results are mainly focused on the evaluation of potential trends of e‐customer preferences. Furthermore, results of a benchmarking analysis are also presented, based on the evolution of satisfaction levels for the quality characteristics of the provided services.

Future research in the context of the presented study may be focused on satisfaction benchmarking analysis, given the rapid changes of the market conditions. An extended satisfaction survey will give the ability to analyze customer preferences in comparison with the main ISP competitors.

The presented study may help organizations in highly competitive markets dominated by rapid technological progresses to track short‐term changes of customer preferences.

The analyses presented are based on the combination of multicriteria analysis and non‐parametric statistics. The main advantage of these methods is that they respect the qualitative (ordinal) form of collected survey data.

The purpose of this article is to present a case study that documents how management science techniques (in particular data envelopment analysis) can be applied to performance improvement initiatives in an inpatient physical therapy setting.

The data used in this study consist of patients referred for inpatient physical therapy following total knee replacement surgery (at a medium‐sized medical facility in the Midwestern USA) during the fiscal year 2002. Data envelopment analysis (DEA) was applied to determine the efficiency of treatment, as well as to identify benchmarks for potential patient improvement. Statistical trends in the benchmarking and efficiency results were subsequently analyzed using non‐parametric and parametric methods.

Our analysis indicated that the rehabilitation process was largely effective in terms of providing consistent, quality care, as more than half of the patients in our study achieved the maximum amount of rehabilitation possible given available inputs. Among patients that did not achieve maximum results, most could obtain increases in the degree of flexion gain and reductions in the degree of knee extension.

The study is retrospective in nature, and is not based on clinical trial or experimental data. Additionally, DEA results are inherently sensitive to sampling: adding or subtracting individuals from the sample may change the baseline against which efficiency and rehabilitation potential are measured. As such, therapists using this approach must ensure that the sample is representative of the general population, and must not contain significant measurement error. Third, individuals who choose total knee arthroplasty will incur a transient disability. However, this population does not generally fit the World Health Organization International Classification of Functioning, Disability and Health definition of disability if the surgical procedure is successful. Since the study focuses on the outcomes of physical therapy, range of motion measurements and circumferential measurements were chosen as opposed to the more global measures of functional independence such as mobility, transfers and stair climbing. Applying this technique to data on patients with different disabilities (or the same disability with other outcome variables, such as Functional Independence Measure scores) may give dissimilar results.

This case study provides an example of how one can apply quantitative management science tools in a manner that is both tractable and intuitive to the practising therapist, who may not have an extensive background in quantitative performance improvement or statistics.

DEA has not been applied to rehabilitation, especially in the case where managers have limited data available.