Bank Lending Decisions Using Projections: A Case-study Approach

This paper, by emphasizing on the process and dynamics of bank corporate credit decisions, presents a description of an introductory case-study suitable for an undergraduate/MSc banking or business finance course.  The case exists in two parts and is designed for instructors to be able to use only those parts which they consider appropriate to their objectives and time considerations. In two or three seventy-five minute classes—depending on the parts of the case utilized and the time allowed for student interaction—instructors can have students explore the factors that influence the lending decision and focus on the interpretation of the mechanical process used in constructing projected financial statements for the identification of the actual borrowing needs of a specific company.

Empirical Testing Of Random Walk Of Euro Exchange Rates: Evidence From The Emerging Markets

This paper utilizes the new non-parametric variance ratio tests based on signs and ranks to examine the random walk hypothesis of Euro exchange rates for 10 Middle Eastern and North African (MENA) currencies.  The results of the new- variance ratio tests reject the random walk hypothesis for all currencies except the Kuwaiti and the Emirate currencies.  Given the improved size and power properties of Wright’s (2000) ranks and signs tests, the results of the new variance ratio tests are robust to the results of the traditional LOMAC variance ratio tests.&nbsp

Empirical Testing Of Different Alternative Proxy Measures For Firm Size

This paper examines the relationship among total sales revenue, total assets, book value of equity, and market value of equity for different economic sectors and timeperiods.  Five statistical tools are used to examine the relationship among the different proxies of size of the firm for the period 1999-2002. Our study shows that the relationships among the four measures of the size of the firm are not the same for the different economic sectors and are not stable over time for each economic sector.  Our results suggest that the use of the four measures interchangeably as a proxy for the firm size may not be appropriate

Energy Efficient Uplink Transmission in Cooperative mmWave NOMA Networks with Wireless Power Transfer

In 5G wireless networks, cooperative non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) are efficient ways to improve the spectral efficiency (SE) and energy efficiency (EE). In this paper, a new cooperative NOMA scheme with WPT is proposed, where EE optimization with a constrained maximum transmit power and minimum required SE is considered for the user grouping and transmit power allocation of users. We obtain a sub-optimal solution by decoupling the original problem in two sub-problems: an iterative algorithm is considered for the user grouping, while, in addition, we utilize the Bat Algorithm (BA) for solving the power allocation problem, where BA was proved to be able to achieve a higher accuracy and efficiency with respect to other meta-heuristic algorithms. Furthermore, to validate the performance of the proposed system, analytical expressions for the energy outage probability and outage probability of users are derived, confirming the effectiveness of the simulation results. It is demonstrated that the proposed cooperative NOMA with WPT offers a considerable improvement in terms of SE and EE of the network compared to other methods. Finally, the effectiveness of BA in solving the EE optimization problem is demonstrated through a high convergence speed by comparing it with other methods

Taking advantage of Ramadan and January in Muslim countries

Studies have shown that religious beliefs and practice play an important role in influencing share price behaviour. Evidence of a Ramadan effect has been documented in Muslim countries suggesting an increase in mean returns as well as a reduction in volatility during the ninth month of the Islamic calendar. In addition to the Ramadan effect, studies have also documented a January effect in Muslim countries. The current study investigates what happens when the Ramadan effect and the January effect occur at the same time. Controlling for the effects of financial crises and time-varying volatility in returns, the results for individual company data from four countries with sizeable Muslim populations indicate higher returns and lower volatility when these two effects overlap, except in one, arguably more Western country, Turkey

Comparative effects of drug interventions for the acute management of migraine episodes in adults: systematic review and network meta-analysis

Objective: To compare all licensed drug interventions as oral monotherapy for the acute treatment of migraine episodes in adults. Design: Systematic review and network meta-analysis. Data sources: Cochrane Central Register of Controlled Trials, Medline, Embase, ClinicalTrials.gov, EU Clinical Trials Register, WHO International Clinical Trials Registry Platform, as well as websites of regulatory agencies and pharmaceutical companies without language restrictions until 24 June 2023. Methods: Screening, data extraction, coding, and risk of bias assessment were performed independently and in duplicate. Random effects network meta-analyses were conducted for the primary analyses. The primary outcomes were the proportion of participants who were pain-free at two hours post-dose and the proportion of participants with sustained pain freedom from two to 24 hours post-dose, both without the use of rescue drugs. Certainty of the evidence was graded using the confidence in network meta-analysis (CINeMA) online tool. Vitruvian plots were used to summarise findings. An international panel of clinicians and people with lived experience of migraine co-designed the study and interpreted the findings. Eligibility criteria for selecting studies: Double blind randomised trials of adults (≥18 years) with a diagnosis of migraine according to the International Classification of Headache Disorders. Results: 137 randomised controlled trials comprising 89 445 participants allocated to one of 17 active interventions or placebo were included. All active interventions showed superior efficacy compared with placebo for pain freedom at two hours (odds ratios from 1.73 (95% confidence interval (CI) 1.27 to 2.34) for naratriptan to 5.19 (4.25 to 6.33) for eletriptan), and most of them also for sustained pain freedom to 24 hours (odds ratios from 1.71 (1.07 to 2.74) for celecoxib to 7.58 (2.58 to 22.27) for ibuprofen). In head-to-head comparisons between active interventions, eletriptan was the most effective drug for pain freedom at two hours (odds ratios from 1.46 (1.18 to 1.81) to 3.01 (2.13 to 4.25)), followed by rizatriptan (1.59 (1.18 to 2.17) to 2.44 (1.75 to 3.45)), sumatriptan (1.35 (1.03 to 1.75) to 2.04 (1.49 to 2.86)), and zolmitriptan (1.47 (1.04 to 2.08) to 1.96 (1.39 to 2.86)). For sustained pain freedom, the most efficacious interventions were eletriptan and ibuprofen (odds ratios from 1.41 (1.02 to 1.93) to 4.82 (1.31 to 17.67)). Confidence in accordance with CINeMA ranged from high to very low. Sensitivity analyses on Food and Drug Administration licensed doses only, high versus low doses, risk of bias, and moderate to severe headache at baseline confirmed the main findings for both primary and secondary outcomes. Conclusions: Overall, eletriptan, rizatriptan, sumatriptan, and zolmitriptan had the best profiles and they were more efficacious than the recently marketed drugs lasmiditan, rimegepant, and ubrogepant. Although cost effectiveness analyses are warranted and careful consideration should be given to patients with a high risk cardiovascular profile, the most effective triptans should be considered as preferred acute treatment for migraine and included in the WHO List of Essential Medicines to promote global accessibility and uniform standards of care. Systematic review registration: Open Science Framework https://osf.io/kq3ys/

Sizing a renewable-based microgrid to supply an electric vehicle charging station: a design and modelling approach

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius

Techno-economic planning of a fully renewable energy-based autonomous microgrid with both single and hybrid energy storage systems

This paper presents both the techno-economic planning and a comprehensive sensitivity analysis of an off-grid fully renewable energy-based microgrid (MG) intended to be used as an electric vehicle (EV) charging station. Different possible plans are compared using technical, economic, and techno-economic characteristics for different numbers of wind turbines and solar panels, and both single and hybrid energy storage systems (ESSs) composed of new Li-ion, second-life Li-ion, and new lead–acid batteries. A modified cost of energy (MCOE) index including EVs’ unmet energy penalties and present values of ESSs is proposed, which can combine both important technical and economic criteria together to enable a techno-economic decision to be made. Bi-objective and multi-objective decision-making are provided using the MCOE, total met load, and total costs in which different plans are introduced as the best plans from different aspects. The number of wind turbines and solar panels required for the case study is obtained with respect to the ESS capacity using weather data and assuming EV demand according to the EV population data, which can be generalized to other case studies according to the presented modelling. Through studies on hybrid-ESS-supported MGs, the impact of two different global energy management systems (EMSs) on techno-economic characteristics is investigated, including a power-sharing-based and a priority-based EMS. Single Li-ion battery ESSs in both forms, new and second-life, show the best plans according to the MCOE and total met load; however, the second-life Li-ion shows lower total costs. The hybrid ESSs of both the new and second-life Li-ion battery ESSs show the advantages of both the new and second-life types, i.e., deeper depths of discharge and cheaper plans

Planning a hybrid battery energy storage system for supplying electric vehicle charging station microgrids

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction