Purpose -This paper seeks to explore the reasons for the dominance of Western accounting and neglect of Islamic accounting in Islamic countries, using Jordan as a case study. Design/methodology/approach -The paper reports the results of a series of interviews, using a semi-structured questionnaire, with senior members of the accounting regulatory regime in Jordan. The interview data are supplemented by relevant secondary (documentary) data. Findings -The paper concludes that economic dependency on developed Western nations and their international agencies is the major factor determining accounting policy and practice in Jordan.Research limitations/implications -The main limitations of this study are the uncertainty concerning the extent to which the respondents' views are representative of accounting policy makers in Jordan, and the inevitable degree of subjectivity involved in evaluating the relative impact of economic dependency and other factors on accounting policy in Jordan. Originality/value -The paper enhances understanding of the neglect of Islamic accounting in Islamic countries and provides insights into the prospects for and barriers to wider adoption of Islamic accounting in future.

Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples that may be crucial to the learning process. We present a linear time resampling method based on random data partitioning and a majority voting rule to address both concerns, where an imbalanced dataset is partitioned into a number of small subdatasets, each of which must be class balanced. After that, a specific classifier is trained for each subdataset, and the final classification result is established by applying the majority voting rule to the results of all of the trained models. We compared the performance of the proposed method to some of the most well-known oversampling and undersampling methods, employing a range of classifiers, on 33 benchmark machine learning class-imbalanced datasets. The classification results produced by the classifiers employed on the generated data by the proposed method were comparable to most of the resampling methods tested, with the exception of SMOTEFUNA, which is an oversampling method that increases the probability of overfitting. The proposed method produced results that were comparable to the Easy Ensemble (EE) undersampling method. As a result, for solving the challenge of machine learning from class-imbalanced datasets, we advocate using either EE or our method.

No abstract

One of the most difficult problems analysts and decision-makers may face is how to improve the forecasting and predicting of financial time series. However, several efforts were made to develop more accurate and reliable forecasting methods. The main purpose of this study is to use technical analysis methods to forecast Jordanian insurance companies and accordingly examine their performance during the COVID-19 pandemic. Several experiments were conducted on the daily stock prices of ten insurance companies, collected by the Amman Stock Exchange, to evaluate the selected technical analysis methods. The experimental results show that the non-parametric Exponential Decay Weighted Average (EDWA) has higher forecasting capabilities than some of the more popular forecasting strategies, such as Simple Moving Average, Weighted Moving Average, and Exponential Smoothing. As a result, we show that using EDWA to forecast the share price of insurance companies in Jordan is good practice. From a technical analysis perspective, our research also shows that the pandemic had different effects on different Jordanian insurance companies.

The objectives of this study are to determine the level of conformity between Current Issued Reports (CIRs) and Integrated Report (IR) elements of the Amman Stocks Exchange (ASE) listed companies, as well as to determine whether the investigated corporate characteristics (size, age, quality assurance (QA), earning per share (EPS), industry type, foreign ownership (FO)) of these companies have any impact on the conformability of CIRs. It is worth mentioning that (QA), and (EPS), have never been examined by looking at its association with corporate disclosures, and IR in particular. Based on adoption of the IR framework and using the method of content analysis, corporate annual reports and other stand-alone reports of 82 companies in 2017 and 2018 within the financial, industrial, and services sectors, were chosen for this study. The findings of the study provide an answer to the research question and show that sectors vary in their levels of conformity. It reveals that the service sector shows the lowest conformability compared to other sectors, whereas the financial firms conform 65%, followed by the industrial sector. It also finds a positive association between CIRs conformability and variables of size, age of company and quality assurance. However, EPS, FO and type of industry were found to have no impact on the conformability of CIRs to the IR framework. This study has contributed to IR research, which, as a field, has previously received very little recognition among scholars in Jordan. Moreover, IR still does not exist in Jordan’s business practices.