RFOS - Repository of Faculty of Organizational Sciences Univ. of Belgrade
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A Novel IBA-DE Hybrid Approach for Modeling Sovereign Credit Ratings
No full textNowadays, the sovereign credit rating is not only an index of a country's economic performance and political stability but also an overall indicator of development and growth, as well as the trust factor that is associated with the country. Due to its importance, the vast amount of available information, and the lack of a closed-form solution, prediction models based on machine learning (ML) and computation intelligence (CI) techniques are being increasingly used to complement traditional financial approaches. In this paper, we aim to introduce a novel ML-CI approach for sovereign credit rating prediction based on a differential evolution (DE) algorithm and interpolative Boolean algebra (IBA). In fact, the proposed approach is based on a pseudo-logical function in the IBA framework derived from the historical data of publicly available indicators using the DE algorithm. Such functions are easily interpreted and enable a subtle gradation among countries. It is shown that the IBA-DE approach outperforms back-propagation neural networks on the observed problem while also providing a deeper insight into each of the indicators used for prediction and its respective influence on the prediction rating on the other
Standards and Standardization Practices: Does Organization Size Matter?
No full textIncrements in economic efficiency resulting from the application of standards generate economic benefits for both producers and consumers. As a result, it is of the utmost importance for organizations to be aware of the benefits that standards bring to their operations. This paper deals with specific categories of standardization effects that organizations can achieve in the processes of formal standardization. The goal was to rank organizations by size, based on the effects that they can gain by getting involved in formal standardization. The data gathered from a survey of experts from the Institute for Standardization of Serbia form the basis of our multicriteria analysis of standardization indicators for micro, small, medium-sized, and large organizations. The final ranking for determining the achievement of standardization effects in different-sized organizations was performed using the PROMETHEE-GAIA method. Our analysis showed that micro organizations were the best performers since they are more flexible than the other size categories of organizations. In contrast, the other types of organizations have lower significant preferences concerning all the criteria. Finally, one of the conclusions is that all observed organizations have the potential to achieve the effects of standardization, although they may differ
A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data
No full textThis paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods
The Characterization of Substructures of c-Anti Fuzzy Subgroups with Application in Genetics
No full textFuzzy and anti fuzzy normal subgroups are the current instrument for dealing with ambiguity in various decision-making challenges. Thiis article discusses gamma-anti fuzzy normal subgroups and gamma-fuzzy normal subgroups. Set-theoretic properties of union and intersection are examined and it is observed that union and intersection of gamma-anti fuzzy normal subgroups are gamma-anti fuzzy normal subgroups. Employee selection impacts the input quality of employees and hence plays an important part in human resource management. The cost of a group is established in proportion to the fuzzy multisets of a fuzzy multigroup. It was a good idea to introduce anti-intuitionistic fuzzy sets and anti-intuitionistic fuzzy subgroups, as well as to demonstrate some of their algebraic features. Product of gamma-anti fuzzy normal subgroups and gamma-fuzzy normal subgroups is defined, the product's algebraic nature is analyzed, and the Sndings are supported by presenting gamma-anti typical sections with blurring and gamma-ordinary parts with the weirdness of well-defined and well-established groups of genetic codes
Strategic Turnaround in the Paper Industry: A New Model for the Procurement of Recycled Paper
No full textIn recent decades, the paper industry has undergone many changes, making this industry more lucrative, "greener", and thus more attractive. Today, recycled paper is a key raw material for paper production. However, this intense growth has also increased the number of market players, making competition more intense and dynamic and causing frequent turmoil. In such an environment, planning procurement and forecasting the price of recycled paper is a big challenge, even for highly experienced procurement managers. In addition, paper production itself is a dynamic process that requires all key grades of recycled paper to be available at all times. Accordingly, managing the optimal level of recycled paper stocks is also a difficult task faced by the procurement unit. The goal of this paper is to address these challenges by developing a new model for the sustainable procurement of recycled paper in the paper industry with the help of the principles of strategic management. Specifically, we aim to enhance our understanding of the factors driving the complexity of recycled paper procurement management. To this end, we conducted a comparative case study in four European companies. To build cases, we collected secondary data on the sampled companies as well as primary data from interviews with top executives at these companies. On the basis of the results from the comparative case study, we propose a new model for the procurement of recycled paper that helps increase the accuracy of forecasting price trends and by extension overall procurement performance. In a nutshell, this paper seeks to improve procurement processes by reducing the complexity of the enterprise procurement unit as well as by offering guidelines for the maintenance of optimal stock levels of recycled paper
FAIR: Fair adversarial instance re-weighting
No full textWith growing awareness of societal impact of artificial intelligence, fairness has become an important aspect of machine learning algorithms. The issue is that human biases towards certain groups of popu-lation, defined by sensitive features like race and gender, are introduced to the training data through data collection and labeling. Two important directions of fairness ensuring research have focused on (i) instance weighting in order to decrease the impact of more biased instances and (ii) adversarial training in order to construct data representations informative of the target variable, but uninformative of the sensitive attributes. In this paper we propose a Fair Adversarial Instance Re-weighting (FAIR) method, which uses adversarial training to learn instance weighting function that ensures fair predictions. Merging the two paradigms, it inherits desirable properties from both interpretability of reweighting and end-to-end trainability of adversarial training. We propose four different variants of the method and, among other things, demonstrate how the method can be cast in a fully probabilistic framework. Additionally, theoretical analysis of FAIR models' properties is provided. We compare FAIR models to ten other related and state-of-the-art models and demonstrate that FAIR is able to achieve a better trade-off between accuracy and unfairness. To the best of our knowledge, this is the first model that merges reweighting and adversarial approaches by means of a weighting function that can provide inter-pretable information about fairness of individual instances
Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies
No full textGlobal financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook's Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators
COVID-19 and profitability of hotel companies in the Republic of Serbia
No full textPojava pandemije COVID-19 pored negativnih posledica na zdravlje ljudi, ostavila je značajne negativne posledice i na ekonomiju. Jedna od grana najviše pogođena pojavom pandemije je turizam i ugostiteljstvo. Cilj ovog rada je da se sagleda da li je sa nastankom pandemije COVID-19 došlo do promene nivoa profitabilnosti hotelijerskih preduzeća u Republici Srbiji. Uzorak istraživanja činilo je 100 hotelijerskih preduzeća iz Republike Srbije, pri čemu je posmatrano njihovo poslovanje u 2019. i 2020. godini. Za potrebe merenja profitabilnosti korišćena je stopa poslovnog dobitka, stopa neto dobitka, stopa prinosa na poslovnu imovinu i stopa prinosa na kapital. Istraživanje je pokazalo da je kod najvećeg broja posmatranih hotelijerskih preduzeća u 2020. godini došlo do smanjenja vrednosti kod sva četiri korišćena pokazatelja profitabilnosti. Primenom Vilkoksonovog testa ranga i t-testa uparenih uzoraka utvrđeno je da su navedena smanjenja korišćenih pokazatelja profitabilnosti statistički značajna.Besides obvious negative consequences for people's health, the COVID-19 pandemic placed significant negative consequences on the economy as well. Since pandemic made tourism and travel almost impossible, these industries of tourism and hospitality suffered the most. The aim of this paper is to assess whether the onset of the COVID-19 pandemic has caused a change in the level of profitability of companies from hotel industry in the Republic of Serbia. The research sample consisted of 100 companies from hotel industry in the Republic of Serbia, where their operations in 2019 and 2020 were observed. The Operating Profit Margin, Net Profit Margin, Return on Assets and Return on Equity were used for the purposes of measuring profitability. The research determined that in the largest number of observed hotel companies in 2020, there was a decrease in profitability considering all four used indicators. By applying the Wilcoxon rank test and the t-test of paired samples, it was determined that the mentioned reductions in the used profitability indicators are statistically significant
Achieving MAX-MIN Fair Cross-efficiency scores in Data Envelopment Analysis
No full textAlgorithmic decision making is gaining popularity in today's business. The need for fast, accurate, and complex decisions forces decision-makers to take advantage of algorithms. However, algorithms can create unwanted bias or undesired consequences that can be averted. In this paper, we propose a MAX-MIN fair cross-efficiency data envelopment analysis (DEA) model that solves the problem of high variance cross-efficiency scores. The MAX-MIN cross-efficiency procedure is in accordance with John Rawls's Theory of justice by allowing efficiency and cross-efficiency estimation such that the greatest benefit of the least-advantaged decision making unit is achieved. The proposed mathematical model is tested on a healthcare related dataset. The results suggest that the proposed method solves several issues of cross-efficiency scores. First, it enables full rankings by having the ability to discriminate between the efficiency scores of DMUs. Second, the variance of cross-efficiency scores is reduced, and finally, fairness is introduced through optimization of the minimal efficiency scores
Conceptual model for exploring the factors which impact reaching the voice of customers
No full textSince the voice of the customer (VoC) could be of core importance for an organisation's success, it is of high significance to identify the motivating and demotivating factors that influence the customers' intention to provide or not to provide feedback on service quality. This study aims to observe how awareness on the issue of providing feedback, personal beliefs on the impact of feedback, expectations from the company, and organisational culture impact the customers' decision to leave feedback. To explore the influence of the chosen factors an online survey was conducted and the Structural Equation Modeling (SEM) analysis was employed. The results show that awareness and organisational culture have a positive impact on the customers' decision to provide feedback, while the expectations from the company have a negative impact on such customer behaviour. The presented conceptual model might provide novel viewpoints into the factors which impact customers' behaviour regarding their decisions to provide feedback and initiate further studies on the topic of VoC