In this study, we estimated the performance efficiency of the Jordanian mining and extracting sector based on Data Envelopment Analysis (DEA). The utilized dataset includes 6 out of 15 corporations that reflect around 90% of the total market capitalization under the mining and extracting sector in the Amman Stock Exchange (ASE). The sample consists of 126 observations from 2000 to 2020. It should be noted that estimating the efficiency of the sector based on time series for each company is not mentioned in the literature review. Therefore, we applied BCC (Banker–Charnes–Cooper) models to estimate performance efficiency and compared between input and output models under DEA. We also estimated the average performance efficiency of the sector to detect weaknesses/strengths among companies. The market capitalization and the operating revenue are used to evaluate the companies’ performance. In addition to the performance variables as output to the DEA models, the current assets, non-current assets, operating expenses, and general administrative expenses are also used as input variables under the DEA models. This study also examined the effect of Gross Domestic Product (GDP) growth and Return on Assets (ROA) on performance efficiency scores for BCC models. In the results, we found that there are differences in performance efficiency across time series in each company based on dynamic BCC models. It is observed that the performance efficiency of NAST Company is better than the other companies based on BCC (Input/output). The GDP growth and ROA reveal the effect on efficiency performance under BCC models. The proposed model can be used to improve the performance efficiency of companies in stock exchange markets.
In this paper, we employ a unique dataset of actual US dollar (USD) forward positions against a number of currencies taken by so-called commodity trading advisors (CTAs). We investigate the extent to which these positions exhibit a pattern of USD carry trading or other patterns of currency trading over the recent period of ultra-loose US monetary policy. Our analysis indeed shows that USD positions against emerging-market currencies are characterised by a pattern of carry trading. That is, the US dollar, as the loweryielding currency, is associated with short positions. The payoff distributions of these positions, moreover, are found to have positive Sharpe ratios, negative skewness and high kurtosis. On the other hand, we find that USD positions against other developed-market currencies have a pattern completely opposite to carry trading, which is in line with the uncovered interest parity trading; i.e. the lower-yielding (higher-yielding) currency is associated with long (short) positions. * This paper is part of a project called "The Efficiency of Futures Markets". This is a joint cooperation program between Ghent University, Belgium; Queen's University Belfast, UK; and the alternative investment specialist RPM Risk & Portfolio Management AB of Stockholm, Sweden. The program is funded by the European Commission's Marie Curie Actions Industry-Academia Partnerships and Pathways. We would like to thank Per Ivarsson and Alexander Mende for their helpful comments. 1 Generally speaking, carry trade strategies attempt to capitalize on yield differentials between financial instruments. Specifically, carry trades involve investments in higher-yielding instruments financed by borrowings in lower-yielding instruments. Koijen et al. (2013) broadly define a carry of an asset as "its expected return assuming that its price does not change". They find that carry is a common phenomenon existing among a variety of asset classes such as equities, commodities, bonds, treasuries, currencies, credit and index options. More importantly, they demonstrate the ability of carry to predict returns on these asset classes.
Here, the link between the mandatory adoption of International Financial Reporting Standards (IFRS) and Real Earnings Management (REM), as well as Accrual Earnings Management (AEM), will be examined for non-financial listed firms in the London Stock Exchange. Robust regression analysis of the mandatory IFRS adoption will be conducted on the panel data, as well as earnings management using three AEM models and three REM models. Mixed results with respect to the qualities of AEM and REM were notably garnered, with mandatory IFRS adoption positively relating to the Roychowdhury of abnormal cash flow and the Roychowdhury of abnormal production. Meanwhile, the Roychowdhury of abnormal discretionary expenses, standard Jones, and Kothari negatively related to mandatory IFRS adoption, whilst modified Jones showed an insignificant relation to mandatory IFRS adoption. Changes in IFRS adoption and guidelines for UK firms may have an impact on AEM and REM, and, as predicted, mandatory IFRS adoption mostly affects the Kothari model followed by the standard Jones model as proxies for accounting earnings quality.
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