Kaminskyi Andrii, Nehrey Maryna, Komar Mariana,
Volume 31, Issue 4 (11-2020)
Abstract
The aim of the paper is to present a complex risk analysis of investing in agriculture Exchange Trade Funds (ETFs). The specific characteristics of agricultural investments should be taken into account as from the direct financial investments into agricultural ETFs, as for the general portfolio approach applying. To achieve the objectives of the work, the authors structured agriculture ETFs into 6 classes, which represent different types of ETFs. A special sample of 26 agricultural ETFs was formed. A complex risk analysis consisted of applying 5 different conceptual approaches to measuring investment risk. In particular, approaches based on measuring variability, applying the concept of Value-at-Risk are applied. The approach of estimating the shocks of changes in the profitability of the asset class in question is applied. The risk level in the aspect of sensitivity to changes in stock returns, bonds and the uncertainty index EPU is investigated. Built portfolios with minimal risk. Obtained results can be applied for investment decisions
Vitalina Babenko, Olena Rayevnyeva, Dmytro Zherlitsyn, Olena Dovgal, Goncharenko Natalia, Miroshnichenko Tetyana,
Volume 31, Issue 4 (11-2020)
Abstract
The processes of transformation of the energy space, namely the impact of alternative energy resources on it, are characterized by changes in the national economy in general and in the energy market in particular. The results of the analysis confirmed the significant dependence of electricity production indicators on renewable sources and such factors as GDP, CO2 emissions, total electricity production, which requires improvement of organizational and economic bases for policy development of state support for renewable energy technologies in countries with exogenous factors. The interdependence between electricity production from renewable sources and economic indicators in Ukrainian-Chilean relations using macroeconomic multifactor analysis based on the correlation method allowed to identify the most influential factors.
Pavlo Hryhoruk, Nila Khrushch, Svitlana Grygoruk,
Volume 31, Issue 4 (11-2020)
Abstract
Addressing socio-economic development issues are strategic and most important for any country. Multidimensional statistical analysis methods, including comprehensive index assessment, have been successfully used to address this challenge, but they don't cover all aspects of development, leaving some gap in the development of multidimensional metrics. The purpose of the study is to construct a latent metric space based on the use of multidimensional scaling. Based on statistics showing the economic development of Ukrainian regions, two-dimensional space of latent scales was constructed and Ukrainian's regions were positioned in this space. The results were interpreted meaningfully. This use of multidimensional statistical analysis confirms its usefulness for measuring the economic development of regions and allows their comprehensive assessment and comparison.
Zakharin Sergii, Viblyi Petro , Bebko Svitlana , Nahorna Nadiia , Aloshyn Sergiy ,
Volume 31, Issue 4 (11-2020)
Abstract
The results of studies on the development of new statistical and econometric approaches to modeling budget policy is presented. The obtained results are applied on the example of tax revenue modeling. The authors note the importance of ensuring transparency and predictability of state financial policy, the realisticness of economic forecasts, because this is the basis of budget modeling. It is also necessary to take into account the various economic cycles that affect the economic dynamics in a particular period of time and in a particular country (or group of countries). Accounting for various factors, including through the use of mathematical methods, will allow to plan reforms with a scientific position. In particular, this is especially true in connection with the introduction of multi-year budget planning. The most important issue of budget policy is the planning of tax revenues (taxes form 90% of budget revenues). To identify the main threats to the tax base, the phenomenon of “tax passes” was used, which is based on an assessment of the effectiveness of a tax credit. The main participants in the formation of the “gross gap” in the value added tax revenues in Ukraine are shown. A correlation and regression analysis of the natural logarithms of the gross domestic product and tax revenues is carried out. This allowed us to determine the elasticity of tax revenues and GDP in Ukraine. A change in GDP directly affects the amount of tax payments to the budget, and the rate of change of indicators is proportional and changes insignificantly. These results allow us to strategically model the reform of discretionary tax policy mechanisms based on a quantitative assessment of tax gaps and the elasticity of tax payments. The authors were able to substantiate some proposals for reforming the budget policy regarding tax revenues.
Serhieieva Liudmyla, Kovtun Oksana, Opalenko Alla, Ivanylova Oksana,
Volume 31, Issue 4 (11-2020)
Abstract
The article deals with the integrated harmonious structure deviation indicator in the system of post-graduate training, which is constructed according to the rule of the “golden ratio”. Calculated deviation of the indicator of five-sector model that corresponds to the GDP in the post-industrial economy. Selecting components integrated th indicator deviation from the harmonious structure is based on the objective statistics and systematic research of GDP from a five-sector model. According to the proposed method of estimation of structural shifts in the sectoral structure of the educational environment, the integrated harmonious structure deviation indicator for the 2010/11-2018/19 academic years was calculated; the dynamics of the integrated harmonious structure deviation indicator for the GDP of Ukraine and for the higher educational system of Ukraine is compared. The calculation of the integrated harmonious structure deviation indicator in dynamics has led to the conclusion that over the last nine years there has been a tendency to train insufficient number of highly qualified specialists who provide the production of intellectual product, based on the requirements of the knowledge economy.
Oleh Kuzmin, Oksana Zhyhalo, Kateryna Doroshkevych,
Volume 31, Issue 4 (11-2020)
Abstract
Innovative capacity as a potential ability of an enterprise to innovative development is manifested in the process of formation and realization of an innovative product, which can be embodied in various forms. In the article innovation capacity is considered as a complex concept that covers the innovative output of the enterprise and the reserve for providing innovative capacity, which can make the difference between the innovative capacity and the current state of the innovative output of the enterprise.
In order to improve the level of management processes in the enterprise, the article improves the method of evaluation the innovative capacity, which is based on the use of a three-dimensional space model of the dependence of the innovative capacity on the level of loading vectors of technique of the enterprise (X-axis), applied innovative technologies (Y-axis) and resources (Z-axis) using AHP-model (analytical-hierarchical process model) and certain functional dependencies that indicate the state of innovative capacity of the enterprise and allow to identify the reserve for providing innovative capacity.
The system of indicators designed to measure the enterprise's innovation capacity is developed on the basis of the AHP-model (analytical-hierarchical process model), which contains two levels: 1) partial indicators designed to assess the level of loading of vectors of the three-dimensional space model of the enterprise's innovation capacity; 2) generalized indicators by which the level of innovation capacity is determined. The article uses the relative weight of indicators, which is calculated by forming a matrix of judgments and evaluating the components of the vector of its priorities.
Y Aleskerova , Zoia Titenko , H Skrypnyk , O Grytsyna ,
Volume 31, Issue 4 (11-2020)
Abstract
The relevance of the research topic is due to the fact that in the current economic conditions attracting additional investments will ensure the further development of the agricultural sector of the economy. The purpose of the article is to establish a close link between investment attraction and increased agricultural output.
Positive dynamics were found as a result of the analysis of the dynamics of investments in fixed assets in the agricultural sector during the analyzed period, but their fluctuations by years are observed due to the influence of factors of the external and internal environment.
Scientific methods were used in the research process: modeling - to build an investment model for the development of the agricultural sector of the economy; economic and statistical - to assess the dynamics of capital investment; analysis and synthesis - to find out the reasons that cause changes in capital investment.
Results of the research. The result of the study is clearly identified trends in attracting investment in the agricultural sector of Ukraine. The analysis of investment attractiveness on the basis of neoclassical Cobb-Douglas production function is carried out. The obtained model made it possible to predict the volume of production based on the expected values of capital and labor.
Faisal Rasool, Pisut Koomsap, Emérancia Raharisoa, Abdul Qayoom,
Volume 32, Issue 3 (9-2021)
Abstract
In the last decade, customers’ active involvement during product development, commonly referred to as co-creation, has emerged as an effective tool to overcome barriers that keep firms from understanding customer needs. Still in its infancy, many co-creation aspects are under-researched; this may present difficulties in aligning firm goals with their co-creators, often leading to project failure. To make the co-creation process more systematic, a framework is presented in this paper that will allow firms to analyse product attributes before engaging in co-creation, concerning firm capabilities and interests and the capabilities and interests of their co-creators. The results of this analysis will help firms to align their goals with the goals of co-creators. Two exploratory case studies were conducted for illustration.
Ali Zaheri, Mahdi Rojhani, Sandra F. Rowe,
Volume 33, Issue 1 (3-2022)
Abstract
The Project Management Body of Knowledge (PMBOK) is a widely used model of project management based on prior experience. This standard does not distinguish between small and large projects, but small projects, with their limited schedules and budgets, face challenges using the extensive structure proposed by this standard. It has been suggested that the standard can be adapted to each project within its specifications; however, the tailoring procedures are complex, time-consuming, and at times impossible to apply to small projects. The present study examined whether or not the PMBOK is an appropriate model for small projects. To address this issue, a questionnaire was prepared and sent to 134 professional project managers. Analysis of the data confirmed that the assumption that PMBOK is a challenge to small projects was not contradicted. Most participants agreed that the procedure should be tailored to prioritize the standard tools and guiding techniques, in addition to the knowledge areas, for small projects.
Zeinab Rahimi Rise, Mohammad Mahdi Ershadi, Mohammad Javad Ershadi,
Volume 33, Issue 1 (3-2022)
Abstract
Drawing lessons from the Covid-19 pandemic according to literature, this contribution aims to show that greening the United Nations System with stronger environmental considerations, can help to shift the global economy from fossil energy to renewable energy with public-health resilient systems. This contribution starts with highlighting the fact that past economic crises and the implementation of the Sustainable Development Global Agenda have not been able to generate strong institutional arrangements for sustainable development including climate resilience building and public health resilient systems. This allows us to apprehend the possibility that the Covid-19 pandemic crisis may face the same incapacity. In response to these statements, this contribution shares the opinion that institutional reforms within the United Nations System may lead to perennial normative provisions and institutional arrangements able to make sustainable development happen with resilient public-health systems. This note highlights the fall of GHZ emissions during the Covid-19 pandemic. It shows, however, based on the history of the past crisis, that the huge investment being mobilized to recover from the pandemic can quickly absorb GHZ emissions fall. The way out suggested is that both the Global Economy and the Global Public Health agendas can be revisited to be strengthened by stronger environmental considerations. One of our findings is that multilateralism can adopt suitable institutional arrangements in Global Environmental Governance throughout the current global agenda on International Environmental Governance Reform within the United Nations System.
Amirhossein Masoumi, Rouzbeh Ghousi, Ahmad Makui,
Volume 33, Issue 3 (9-2022)
Abstract
Purpose: Non-cancerous prostate lesions such as prostate calcification, prostate enlargement, and prostate inflammation cause too many problems for men’s health. This research proposes a novel approach, a combination of image processing techniques and deep learning methods for classification and segmentation of the prostate in CT-scan images by considering the experienced physicians’ reports.
Methodology: Due to the various symptoms and nature of these lesions, a three-phases innovative approach has been implemented. In the first phase, using Mask R-CNN, in the second phase, considering the age of each patient and comparison with the standard size of the prostate gland, and finally, using the morphology features, the presence of three common non-cancerous lesions in the prostate gland has investigated.
Findings: A hierarchical multitask approach is introduced and the final amount of classification, localization, and segmentation loss is 1%, 1%, and 7%, respectively. Eventually, the overall loss ratio of the model is about 9%.
Originality: In this study, a medical assistant approach is introduced to increase diagnosis process accuracy and reduce error using a real dataset of abdominal and pelvics’ CT scans and the physicians’ reports for each image. A multi-tasks convolutional neural network; also presented to perform localization, classification, and segmentation of the prostate gland in CT scans at the same time.
Shahla Zandi, Reza Samizadeh, Maryam Esmaeili,
Volume 33, Issue 4 (12-2022)
Abstract
A coalition loyalty program (CLP) is a business strategy employed by for-profit companies to increase or retain their customers. One of the operational challenges of these programs is how to choose the mechanism of coordination between business partners. This paper examines the role of revenue sharing contracts in the loyalty points supply chain of a CLP with stochastic advertising-dependent demand where the program operator (called the host) sells loyalty points to the partners of the program. The purpose of the study is to examine the effect of this coordination mechanism on the decisions and profits of the members of the chain using the Stackelberg game method and determine whether the presence of revenue sharing contracts benefits the chain members when the advertising is done by the host and when the advertising cost is shared between the host and its partners. The results show that when the host gives bonus points to end customers (advertising), revenue sharing contracts become a powerful incentive for the profitability of the host and its partners. The findings provide new insights into the management of CLPs, which can benefit business decision-makers.
Mohammad Yaseliani, Majid Khedmati,
Volume 34, Issue 1 (3-2023)
Abstract
Diagnosis of diseases is a critical problem that can help for more accurate decision-making regarding the patients’ health and required treatments. Machine learning is a solution to detect and understand the symptoms related to heart disease. In this paper, a logistic regression model is proposed to predict heart disease based on a dataset with 299 people and 13 variables and to evaluate the impact of different predictors on the outcome. In this regard, at first, the effect of each predictor on the precise prediction of the outcome has been evaluated and analyzed by statistical measurements such as AIC scores and p-values. The logit models of different predictors have also been analyzed and compared to select the predictors with the highest impact on heart disease. Then, the combined model that best fits the dataset has been determined using two statistical approaches. Based on the results, the proposed model predicts heart disease with a sensitivity and specificity of 84.21% and 90.38%, respectively. Finally, using normal probability density curves, the likelihood ratios have been established based on classes 1 and 0. The results show that the likelihood ratio classifier performs as satisfactorily as the logistic regression model.
Theodore Alvin Hartanto, Seng Hansun,
Volume 35, Issue 3 (9-2024)
Abstract
One method to diagnose retinal diseases is by using the Optical Coherence Tomography (OCT) scans. Annually, it is estimated that around 30 million OCT scans are performed worldwide. However, the process of analyzing and diagnosing OCT scan results by an ophthalmologist requires a long time so machine learning, especially deep learning, can be utilized to shorten the diagnosis process and speed up the treatment process. In this study, several pre-trained deep learning models are compared, including EfficientNet-B0, ResNet-50V2, Inception-V3, and DenseNet-169. These models will be fine-tuned and trained with a dataset containing OCT scanned images to classify four retinal conditions, namely Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), Drusen, and Normal. The models that have been trained are then tested to classify the test set and the results are evaluated using a confusion matrix in terms of accuracy, recall, precision, and F1-score. The results show that the model with the best classification results in the batch size of 32 scenario is the ResNet-50V2 model with an accuracy value of 98.24%, precision of 98.25%, recall of 98.24%, and F1-score of 98.24%. While for the batch size of 64, the EfficientNet-B0 model is the model with the best classification results with an accuracy value of 96.59%, precision of 96.84%, recall of 96.59%, and F1-score of 96.59%.
Emad Hajjat, Majed Alzoubi, Leqaa Al-Othman, Lu'ay Wedyan, Osama Hayajneh,
Volume 35, Issue 3 (9-2024)
Abstract
This study examines the role of forensic accounting in enhancing financial transparency and reducing fraud in Jordanian institutions. Using a mixed-method approach, data were collected from 150 respondents including chartered accountants, auditors, financial managers. through a structured questionnaire. The findings reveal that forensic accounting significantly contributes to fraud prevention by supporting government investigations, providing courtroom testimony), and developing financial management systems. Additionally, forensic accountants play a crucial role in preparing key reports for government activities. The correlation analysis shows strong interdependencies between forensic accounting’s roles in arbitration and fraud detection. While most hypotheses were confirmed, challenges were noted in applying forensic accounting within the public sector. The study concludes by recommending that policymakers strengthen the integration of forensic accounting into Jordan's financial regulatory framework to enhance its effectiveness, particularly in the public sector. This research highlights the vital role of forensic accounting in maintaining financial integrity and provides a foundation for future studies.
