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This special issue of Business Systems Research (SI of the BSR) is being co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI -SOR). It focuses on recent advances in Operations Research and Management Science (OR / MS), with a particular emphasis on linking OR / MS with other areas of quantitative and qualitative methods in the context of a multidisciplinary framework. The ten papers that were chosen for this Special Issue of the BSR present advancements and new techniques (methodology) in the field of Operations Research (OR), as well as their application in a variety of fields, including risk management, mathematical programming, game theory, gravity, spatial analysis, logistics, circular economy, continuous improvement, sustainability, e-commerce, forecasting, Gaussian processes, linear regression, multi-layer perceptron, and machine learning.

This paper examines the relative cost efficiency of U.S. small banks after the 2008 Global Financial Crisis (2008 GFC). Using financial information from 10,495 of the same small banks operating from 2010 to 2021, we examine the after-effects of the recent global financial crises on the U.S. small banks. The study uses Data Envelopment Analysis (DEA) to calculate the overall efficiency using yearly and pooled data. The overall efficiency measure is then decomposed into allocative, technical, pure-technical, and scale efficiency to better understand the sources of small banks’ inefficiencies. The results indicate that the overall efficiency of small banks operating in the U.S. after the 2008 GFC has been continuously low until 2021. The source of the low level of overall efficiency has been the low level of technical efficiency rather than allocative efficiency. In turn, the basis of the low level of technical efficiency has been pure technical and scale efficiency. Understanding the origins of cost inefficiencies in small banks has implications for micro and macro policymaking. Examining the underlying causes of cost inefficiencies in small banks after the financial crisis can inform policymakers in devising strategies to improve banks’ cost efficiency.

The optimization of sustainable smart cities is an essential endeavor in modern urban development, aiming to enhance the quality of life for citizens while minimizing environmental impacts. Big data plays a critical role in achieving these goals by enabling the collection, analysis, and utilization of vast amounts of information to make informed decisions. However, implementing big data in smart cities faces significant barriers, including data-sharing challenges, technical limitations, and organizational non-cooperation. Addressing these barriers is crucial for the successful deployment of smart city initiatives. We propose a novel approach to tackle these challenges using the Improved Zero-Sum Grey Game (IZSGG) theory and the Grey Best-Worst Method (G-BWM). This method comprehensively analyzes the risks and uncertainties associated with big data implementation in smart cities. By modeling the interactions between different stakeholders and their competing interests, IZSGG theory provides a framework to identify optimal strategies for data management. The G-BWM further refines these strategies by evaluating and prioritizing the various factors influencing big data utilization. Our findings reveal that the worst-case scenario for a smart city involves the simultaneous occurrence of several risks, all of which have positive values, indicating their potential to significantly disrupt smart city operations. The specific risks identified include: the sharing of data and information, the collection and recording of data, technical limitations and challenges associated with technology, the non-cooperation of organizations, and issues related to the interpretation of complex information. The technical barrier is the most significant with a weight of w(T)=0.6152, indicating its critical role compared to other barriers. Within this category, the sub-barrier of technical and technological constraints is particularly critical, with a weight of 0.39267375.

Customer segmentation has become one of the most innovative ways which help businesses adopt appropriate marketing campaigns and reach targeted customers. The RFM model and machine learning combination have been widely applied in various areas.

The European Green Deal is a comprehensive strategy aiming to fully decarbonize the economy by 2050, with a key target of a 55 % reduction in carbon emissions by 2030. This study contributes to the debate around the European Green Deal by providing insights from different dynamic stochastic general equilibrium models for the six largest European countries and the Union itself. We discuss four policy targets consistent with a 55 % emissions cut by 2030, using carbon prices as the main policy tool. The study reveals that defining fair climate targets for a group of relatively heterogeneous countries like the European ones is challenging. Simple approaches, such as achieving the same CO2 emissions per capita in 2030 or the same emissions over GDP, lead to vastly different policy efforts measured by carbon prices and welfare losses in our model.Implementing a common set of climate policies and tools defined by the European Commission may seem fair, but it results in varying impacts on emissions across countries. The same carbon price has an asymmetric effect on emissions and welfare. Alternatively, achieving the same emission reduction requires different carbon prices for different countries. For example, Spain needs a carbon price of 151 €/tCO2, while Poland only needs 71 €/tCO2 to achieve the target. This asymmetry sparks a debate on determining the right climate target for each country. The article aims to open this discussion rather than provide a definitive answer.

As finance returns to its fundamental purpose of serving the real economy, its connections with various industries are strengthening. Accurately depicting the interdependence among these industries and mitigating financial risks has become increasingly critical. The dependence among China's real industries is dynamic rather than static, which is particularly pronounced during the COVID-19 pandemic. In this paper, we propose a dynamic factor model to optimize the risk of high-dimensional portfolios. To describe the dependence structure, we employ the factor copula model, driven by a GAS (Generalized Autoregressive Score) model. By combining the dynamic factor model with a mean-ES (Expected Shortfall) model, we construct a dynamic factor copula-mean-ES model. Our empirical findings, based on an analysis of 24 industries in China, suggest that the dynamic heterogeneous factor copula model is the most suitable for describing portfolio risk. Furthermore, the mean-ES model ensures the lowest portfolio risk for a given expected return. Accurate return predictions enable leveraging market information to develop a ''good knowledge'' of dynamic copula and risk optimization. This ''good knowledge'' of dynamic copulas facilitates precise return prediction and effective risk optimization of portfolios, thereby addressing the relationship between risk prevention and sustainability. Moreover, it reveals the internal connection between China's real industry and the risk landscape of the financial market.

The covariance matrix is an important element of many asset allocation strategies. The widely used sample covariance matrix estimator is unstable especially when the number of time observations is small and the number of assets is large or when high-dimensional data is involved in the computation. In this study, we focus on the most important estimators that are applied on a group of Markowitz-type strategies and also on a recently introduced method based on hierarchical tree clustering. The performance tests of the portfolio strategies using different covariance matrix estimators rely on the out-of-sample characteristics of synthetic and real stock data.

The article analyzes the interaction between monetary and fiscal policy in Mexico. We calibrated a semi-structural model for a small open economy, based on Aguilar and Ramírez-Bulos (2018), for Mexico by using quarterly data from 2001Q1 to 2019Q4. The fiscal policy block models the fiscal deficit depending on output, an endogenous sovereign risk premium, a state-owned oil company, and public debt dynamics with domestic and foreign components. We assumed a fiscal rule whereby the deficit has an upper bound. The monetary policy follows a Taylor rule. We study the effects of different shocks on the economy, such as a drop in commodity prices, an expansion of public spending, an increase in the risk premium, a hike in the interest rate, and depreciation of the real exchange rate. We show that, remarkably, the risk premium channel transmits threats from the fiscal block to the monetary block, calling for the central bank to stabilize inflation. By contrast, starting at the economy’s steady state, an exogenous monetary policy shock affects the fiscal block mainly through the interest rate’s influence on the debt service, prompting a fiscal response to stabilize deficit.

The algorithm for optimization of a credit portfolio has not been fully demonstrated. This paper fills the gap in the literature by presenting a general approach for optimizing a credit portfolio by minimizing the default risk of the entire portfolio. Default risk is measured with quadratic weighting and a matrix containing information about the default intensity of two stocks and the correlation in default between them. The default correlation and the default intensity are represented with a novel bivariate intensity model. A multi-objective genetic algorithm is introduced to optimize a credit portfolio with the purpose of overcoming limitations in the analytical method and improving the efficiency of optimization. The algorithm can be applied to a portfolio's credit risk management, which is particularly crucial for investors and regulars in emerging markets.

Aim/purpose – Shelf space is one of the most important tools for attracting customers’ attention in a retail store. This paper aims to develop a practical shelf space allocation model with visible vertical and horizontal categories. and formulate it in linear and non-linear forms.

Background: E-commerce websites have been established expressly as useful online communication platforms, which is rather significant. Through them, users can easily perform online transactions such as shopping or ordering food and sharing their experiences or feedback.

This study utilizes machine learning and, more specifically, reinforcement learning (RL) to allow for an optimized, real-time operation of large numbers of decentral flexible assets on private household scale in the electricity domain. The potential and current obstacles of RL are demonstrated and a guide for interested practitioners is provided on how to tackle similar tasks without advanced skills in neural network programming. For the application in the energy domain it is demonstrated that state-of-the-art RL algorithms can be trained to control potentially millions of small-scale assets in private households. In detail, the applied RL algorithm outperforms common heuristic algorithms and only falls slightly short of the results provided by linear optimization, but at less than a thousandth of the simulation time. Thus, RL paves the way for aggregators of flexible energy assets to optimize profit over multiple use cases in a smart energy grid and thus also provide valuable grid services and a more sustainable operation of private energy assets.

The purpose of this research is to explain the policy model even for the development of renewable energy sources in the country. This article examines the factors affecting the renewable energy market and the development methods of these resources based on the grounded theory. The information of this research has been collected using joint interviews with the managers and experts of the clean energy technologies development headquarters. Considering that renewable energy sources play an important role in the country's energy supply, data analysis is implemented in three different stages including open coding, selective coding, and central coding. This analysis will lead to appropriate development policies in the renewable energy sector. In this regard, it has been tried to look at the renewable energy market as a cause-and-effect market based on the goals set in the mentioned documents of the country and identify the background and intervening factors in this regard. Finally, a paradigmatic model of a suitable policy for renewable energy sources will be presented.

This study investigated temporal changes in factor adjustment of the Japanese manufacturing industry by applying a dynamic factor model, in which labor and capital were quasi-fixed to a panel of industries from 1972 to 2012. Estimations show that the adjustment speeds, with which factors approach their optimum levels, have increased over the period. Particularly, factor adjustment rates have significantly increased since 2000. The estimations suggest that Japanese manufacturers have become more flexible in hiring workers and faster in making investments, which reduces adjustment cost significantly. This dynamic gain is ignored from static analysis, underestimating the benefit of labor market reform. The study suggests that policymakers should consider dynamic factor adjustment in assessing policy impacts accurately when implementing an industrial policy.

One-criterion decision making under uncertainty (1-DM/U) is related to situations in which the decision maker (DM) evaluates the alternatives on the basis of one objective, but e.g. due to numerous uncertain future factors some parameters of the problem are not deterministic. Instead of entirely known paramaters, a set of possible scenarios is available. Multi-criteria decision making under certainty (M-DM/C) concerns cases where the DM assesses particular options in terms of many objectives. The parameters are known. Therefore, scenario planning is redundant. Both issues are investigated by many researchers and practitioners, since real economic decision problems are usually at least uncertain or multi-objective. In the paper, numerous analogies between 1-DM/U and M-DM/C are revealed. Some of them have existed for many decades, but others, so far, have not been developed. A careful examination of all the similarities enables an improvement of existing methods and a formulation of new algorithms for 1-DM/U and M-DM/C. The article presents six pairs of similar procedures and contains the description of three novel approaches created by analogy to existing ones.

Purpose – This study investigates the dynamic production structure of the Japanese manufacturing industry by using the adjustment cost approach. The study is to shed some light on the unique dynamic structure of the Japanese manufacturing industry. The study attempts to help design and predict industrial policies that are implemented to enhance domestic investments by the Japanese government. Design/methodology/approach – This study obtains a system of dynamic factor demand and output supply equations by applying the dual approach to the intertemporal value function as represented by the Hamilton–Jacobi equation. By using industrial panel data for 1973–2012 of the Japanese manufacturing industry, the study estimates the system of the behavioral equations and corresponding elasticities. The study uses hypothesis tests and dynamic elasticities to investigate the dynamic structure of the Japanese manufacturing industry. Findings – Estimation results show that labor and capital are quasi-fixed variables that adjust about 0.2 percent annually to the long-run optimum levels. Estimated adjustment rates are very slow as often presumed about the Japanese manufacturing industry, which uses lifetime employment practice and slow decision-making process in investment decisions. The results also show that output supply and factor demand elasticities vary greatly depending on time horizon. Factor demand increases when its own price increases in the short run, suggesting that factor adjustment is mostly determined factor prices in the past due to sluggish factor adjustment. However, factor demand becomes a normal downward-sloping curve in the long run as factor adjustment gets completed. Originality/value – Japanese manufacturing firms hire employees through lifetime contract to exploit the benefits of dynamic learning-by-doing and execute investments carefully considering all the possible impacts. Under the strategy, adjustment costs for changing workers and capital stock are minimized. Dynamic adjustment model is expected to shed some light on the unique dynamic structure of the Japanese manufacturing industry. However, researches regarding the dynamic factor adjustment of the Japanese manufacturing industry are hard to find. This study is expected to fill the research vacuum.

Subject and purpose of work: The purpose of this work was to compare selected portfolio strategies in terms of return rates in order to answer the question whether the method of determining the weights of the portfolio and reduction of the number of portfolio elements characterized by strong positive correlation of rates of return have an impact on its profitability.

In our study, we used a system dynamics model to analyze how frequently discussed transport policy measures affect the total transport performance of long-distance passenger transport in Germany and the transport performance of relevant modes (modal split shares). We then combined the results with established data on mode-specific greenhouse gas emissions to ultimately determine the climate protection effects of the measures.Our analysis shows that an actual modal shift from road to rail will only occur if the price competitiveness and supply conditions of long-distance rail passenger transport are significantly improved. Scenarios for increasing the price of passenger car traffic via a passenger car toll on trunk roads or an additional CO2-based tax on fuels increase the market share of rail, but limit overall transport performance growth compared to our reference scenario with no measures. A doubling of long-distance passenger rail transport cannot be achieved with any of the measures or bundles of measures examined, neither in terms of transport performance nor modal split share. According to our calculations, only the “car repression” scenario makes a noticeable contribution to reducing CO2 emissions. German government's ambitious climate targets for 2030 can only be reached if the climate footprint of vehicles – especially passenger cars – is significantly improved. It is also noteworthy that, according to our analysis, a politically frequently demanded ban on domestic flights would ultimately even lead to an increase in CO2 emissions, since, among other things, general travel costs would fall.

Blockchain technology has gained a great public interest due to the appearance of cryptocurrencies, a digital asset used for exchanging funds. Although blockchain calculations offer the benefits of security and reduced costs, blockchain is still strongly criticised for its lack of usefulness and resource-heavy consumption.

This article highlights the application of the Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) I and II in selecting the best laptop model among six different available models in the market. Seven important criteria, that is, processor, hard disk capacity, operating system, RAM, screen size, brand, and color, are selected, based on which the selection process have been made. Analytic hierarchy process (AHP) is adopted for calculating the weightages of the seven criteria and PROMETHEE is applied to select the best alternative. PROMETHEE I provides the partial ranking and preferences of one model over another, whereas PROMETHEE II provides the complete ranking of the alternatives. From this analysis, Model 4 is coming out to be the best laptop model occupying the first position and Model 1 occupies the last position, thus indicating it as the worst model among the group. The objectives of this article are to select the best laptop model among six available alternatives and to understood the steps of both multiple criteria decision-making (MCDM) methodologies, that is, PROMETHEE and AHP, in details.

Research background: Following a rational health policy by the state is tantamount to having knowledge of society’s health status and health affecting factors. It has become particularly important to focus on the 21st century diseases resulting in premature deaths. That objective dominated the 2016 Act on Public Health and the 2016–2020 National Health Programme connected with it.

In most countries, immigrant and native students perform differently in the Programme for International Student Assessment (PISA) due to two main reasons: different immigration regimes and differences in their home-country educational systems. While there is sophisticated literature on the reasons for these performance gaps, it is barely considered in the educational efficiency research. Our approach distinguishes between selection effects caused by immigration policies, and the efficiency of educational systems in integrating immigrant students, given their socio–economic background. Accordingly, we split our sample, which consists of 153,374 students in 20 countries, calculate various different efficient frontiers, and ultimately decompose and interpret the resulting efficiency values. We find large differences in educational system efficiency, when controlling for negative selection effects caused by immigration regimes.

Among the many factors that cause project delays or cancellations are disruptions, that is, unforeseen events occurring during the implementation of a project, which postpone or interrupt the performance of project activities. Examples of disruptions include employee absenteeism, addition of new activities, and others. One way to deal with this type of events is to predict potential disruptions and prepare redundant resources to be used should a disruption occur (proactive approach). The focus of the present paper are human resources, in particular redundant project team competence frameworks, which allow to continue work on a project in the event of a disruption. Previous studies on planning competence frameworks regard insensitivity (robustness) to one type of disruption, caused by employee absenteeism (an absence of one, two, or three employees). The goal of this article is to present a proactive procedure that allows to seek competence frameworks robust to two types of disruptions: absence of one employee and addition of new activities not included in the project plan. Examples are provided to illustrate how the proposed approach can be used in practice.

The main goal of this research is to analyse the investment benefits from an incorporation of the volatility exposure to the diversified portfolio from the perspective of a Polish investor. Volatility, treated as a new asset class, may improve the performance of the portfolio due to its negative correlation with most types of assets. This topic has been widely investigated for the United States and Europe whereas the Polish market appears to be not heavily researched and this study may fill this gap. The research covers the period from October 2010 to July 2018 and is performed on daily close prices. To construct the portfolios the analysis uses the mean-variance framework and the naïve diversification approach. The comparison of risk-adjusted returns between investments with and without volatility exposure enables an answer to the research question about an improvement of the results by the addition of a non-standard asset to the diversified portfolios. The VXX is considered as the proxy for volatility as it is the most popular ETN which follows the volatility index derivatives with the given maturity. To test the robustness of the results the portfolios are constructed with a broad range of different parameters and assumptions imposed on the optimization procedure.

The paper deals with the management of product information in a cosmetics retail and information interaction of a retailer with distributors and manufacturers. We developed a list of requirements for the types of information for a product catalogue. Using a survey and questionnaire of employees of the retailer, we determined what information the departments needed. We made sure that one standard to the structure of information did not exist and proposed our own scheme. Based on the survey results, we created a list of requirements for the information management system and proposed a scheme of system components. We conducted a comparative analysis of the costs of the company (about 2000 employees) for the salary of employees involved in entering information before and after the implementation of our system. For evaluation of the effectiveness of the integration of the developed system, we defined the cost indicators: PP, NPV and IRR.

Croatian Industrial Confidence Indicator (ICI) is one of the measures of mangers’ sentiment about the economic situation in the Croatian manufacturing industry. Since 2005, the ICI has been calculated in accordance with the harmonised European Commission methodology as a simple average of three variables: order books, stocks of finished products and production expectation. It was empirically confirmed that the ICI could predict the direction of change in industrial production more than one month ahead. With the aim of raising the ICI forecasting power, this paper proposes a novel ICI with a different weighting scheme. The empirical analysis is based on monthly data for three standard ICI subcomponents and industrial production expressed as year-on-year growth rates. The data set covers the period from May 2008 to February 2019. Data sources were the European Commission and Eurostat. The newly defined ICI was constructed by using the nonlinear optimisation approach. The weights were determined by minimizing the root mean square error (RMSE) in a simple regression model and by maximizing the correlation coefficient between the ICI and industrial production for various time lags. The results reveal that the newly defined ICI performs better in adapting and following the industrial production growth rate as well as that the dominant component in the ICI is the production expectation.

The quadratic trend is a statistical model described by the quadratic function. Finding its extremum (also called the vertex or the turning point) using differential calculus or completing the square method is very well known in the literature. In this paper, a new method for finding the extremum of the quadratic function, based on a simple mathematical inequality is proposed. In comparison with the other two known methods, our method does not require the differentiability assumption and it takes fewer steps than completing the square method. Also, it is shown how the turning point for the quadratic trend can be applied in forecasting the unemployment rate in Croatia in the first quarter of 2019. The obtained conclusions are equal to the conclusions obtained in the usual way by using forecasting software.

Research background: When selecting effective portfolios, the portfolio risk is minimized at the given expected return rate or the expected return rate is maximized with a given risk level. However, it is also worth using additional information, such as fundamental and market indicators to examine the companies’ economic and financial situation. Taking into account the chosen indicators, the initial selection of companies can be approached as a multi-criteria problem. Besides, the choice of the period from which data will be taken gives the opportunity to use non-standard tools.

The optimal procurement is achieved by monitoring them from the very beginning of the planning process. Procurement planning, as one of the basic functions of procurement management, determines the goals, tasks and the way of their realization. The realization of procurement plans is realized through the purchase function of business entities, both from the domestic and foreign markets.

The paper is focused on specific location-allocation problem aimed to determine a set of scrap yards for vehicle decommissioning in Slovakia. The goal is to determine scrap yards network so that it is not prohibitive to pass old car for dismantling and further processing wherever former owner lives. Two approaches are considered. Once we consider the case when it is necessary to construct a completely new network of scrap yards, which results to setting of their minimum numbers and also their location. In the latter case, the already existing network of scrap yards is considered, while the model provides its extension, in order to achieve the desired values of accessibility for all residents. The results were applied to an existing network of scrap yards identifying locations to build new scrap yards. Areas where whole new network of scrap yards must be built were also identified.

Trading electricity across market zones furthers competitive power prices, security of supply and the integration of renewable energy. In the European Union, flow-based market coupling is the target model to compute correct trading capacities between markets, while approximating physical grid constraints. The methodology relies on predictive, carefully designed parameters to maximize trading opportunities, while keeping congestion management at acceptable levels. This paper explains the fundamentals of flow-based market coupling. It provides an open-access model based on a test network and its data. By providing a guide to the theory and conducting several case studies, the functioning and effects of the most influential parameters are demonstrated. Innovative aspects of this paper are its illustrative nature and its answer to the following research questions: (1) What is the effect of (a) generation shift keys and (b) threshold choice on the selection of critical network elements? (2) What is the effect of (a) generation shift keys, (b) selected critical network elements and (c) security margins on market coupling and congestion management results? It also addresses the effect of higher shares of renewable energy and minimum trading capacities on flow-based market coupling. This analysis shows that (1) the effects of flow reliability margins and selected critical network elements dominate the effect of generation shift keys on flow-based market coupling domains, (2) power systems with an increasing share of renewable energy may necessitate re-adjusted parameters and (3) minimum trading capacities can be guaranteed by redispatch or altering the base case.

This paper proposes a measure of environmental performance in the treatment of municipal solid waste, which is defined as a ratio between a composite indicator of waste treated through environmentally desirable operations –recycling and recovery in our case study– and a composite indicator of waste treated through undesirable operations –landfill and incineration. Moreover, it contributes both overall and treatment-specific indicators of performance. Data Envelopment Analysis (DEA) techniques are used to compute the environmental performance indicators and they are illustrated with an empirical assessment of the environmental performance of the European Union-28 (EU-28) members in their treatment of municipal waste, with data for the year 2017. Our results point to a worryingly low average level of performance, with the best performers being mainly Nordic and Central European countries such as Sweden, Germany, Belgium, Finland and Denmark; at the opposite end of the spectrum, environmental performance in the treatment of waste is particularly low in most of the Eastern European countries that joined the EU-28 from the 2000s, and some Southern member states. The determinants of performance are also investigated, the main finding being a positive and statistically significant association between environmental performance in municipal waste treatment and the level of economic development.

Abstract:: The recent coronavirus outbreak has made governments face an inconvenient trade-off choice, i.e. the choice between saving lives and saving the economy, forcing them to make immensely consequential decisions among alternative courses of actions without knowing what the ultimate results would be for the society as a whole. This paper attempts to frame the coronavirus trade-off problem as an economic optimization problem and proposes mathematical optimization methods to make rationally optimal decisions when faced with trade-off situations such as those involved in managing through the recent coronavirus pandemic. The framework introduced and the method proposed in this paper are on the basis of the theory of rational choice at a societal level, which assumes that the government is a rational, benevolent agent that systematically and purposefully takes into account the social marginal costs and social marginal benefits of its actions to its citizens and makes decisions that maximize the society's well-being as a whole. We approach solving this trade-off problem from a static as well as a dynamic point of view. Finally, we provide several numerical examples clarifying how the proposed framework and methods can be applied in the real-world context.

Sound knowledge of travel behavior is important for many findings and decisions in science, business and transport policy. This is not only about spatial distribution, but also and above all about the choice of transport mode. Anonymized mobile network data (MND) can provide valuable information here. This relatively new method is fundamentally superior to other approaches in many respects, but a comprehensive scientific evaluation is still pending. In this context, the present study should help to close research gaps by comparing the results of an MND analysis with the output of an existing transportation model based on System Dynamics. The comparison includes the number of trips within one year, the choice of means of transport and the spatial distribution in long-distance passenger transport within Germany (for all trips with a distance of 50 km or more). We found that the results for travel frequency and modal split were very similar at high aggregation (total annual values for the study area). Also, for distance categories (with an interval of 100 km each) the deviations were very small. However, there were significant differences for origin-destination pairs. In order to be able to make a well-founded comparison here, more in-depth research is necessary in the future.

This paper analyzes empirically the performance gains of using high frequency data in portfolio selection. Assuming Constant Relative Risk Aversion (CRRA) preferences, with different relative risk aversion levels, we compare low and high frequency portfolios within mean-variance, mean-variance-skewness and mean-variance-skewness-kurtosis frameworks. Using data on fourteen stocks of the Euronext Paris, from January 1999 to December 2005, we conclude that the high frequency portfolios outperform the low frequency portfolios for every out-of-sample measure, irrespectively to the relative risk aversion coefficient considered. The empirical results also suggest that for moderate relative risk aversion the best performance is always achieved through the jointly use of the realized variance, skewness and kurtosis. This claim is reinforced when trading costs are taken into account.

Nowadays, when marketing and branding change, companies are trying to find new ways to evaluate the effectiveness of their marketing activities as they impact on current and future business results. The main objective of the contribution is to evaluate the effectiveness of decision making unit (DMU) production units in the form of selected Slovak brands through the non-parametric data envelopment analysis (DEA) method. The sample size consists of 10 Slovak brands (Slovenská sporiteľňa, VÚB banka, Tatra banka, ESET, Slovnaft, Matador, Rajec, Sygic, Sedita and Zlatý bažant). Through DEA, we have gained a portfolio of effective and portfolio of ineffective brands operating in Slovakia. Depending on the choice of the DEA model, effective brands included Slovenská sporiteľňa, Sygic, Zlatý Bažant, Rajec and Sedita. Other brands were classified as ineffective. The result for ineffective brands is the creation of archetypal characters that we propose in Results section. The part of Results section is the focus of the businesses of the brands in question on the use of social media, to a larger extent, create a social media voice so that the personality of the brand is reflected in contributions in social media communicating on Facebook, Instagram or Twitter. In contribution, deduction, induction, analysis and marketing research methods were used.

Objetivo: Este artículo desarrolla un modelo microeconómico estocástico útil para explicar el comportamiento de una madre cuando es el único participante en el ingreso familiar.Metodología: Se propone un modelo en el que la jefa de familia tiene un consumo de subsistencia sujeto a dos restricciones, la primera considera un ingreso incierto junto con depósitos en cajas de ahorro cuando hay un excedente, y la segunda consiste de su hábito (rutina) de consumo. Alcance: A través del modelo propuesto se determinan la proporción óptima del ingreso que la jefa de familia destina al ahorro cuando hay un excedente, las horas que destinará a su actividad laboral y la estrategia óptima de consumo futuro. Resultados: A través de un modelo VAR se muestra evidencia empírica, para el caso mexicano, que confirma los resultados de la propuesta teórica utilizando datos de consumo, salarios y el número de madres solteras, entre otras variables, durante 2005-2016. Para ello se examinan la causalidad de Granger mediante el procedimiento de Toda-Yamamoto y se analizan las funciones de impulso-respuesta. Se estudia también el efecto de la estabilidad de la tasa de interés sobre el consumo de las jefas de familia en el periodo 2009-2017. Conclusiones: El concepto de formación de hábitos juega un papel fundamental en la toma de decisiones de la jefa de familia ya que su consumo pasado sí influye en su consumo futuro.

The objective of the European railway reforms which started in the 1990s was to enable the entry of competition to raise the efficiency of railway transport. This was undertaken because the level of services, particularly in former Eastern Bloc countries, was very low due to neglected investment, and so railway transport market share was in decline. The primary goal of this text is to determine based on available empirical studies whether the reforms affected the efficiency of the railway sector positively, and the secondary aim is to identify the factors which complicate evaluation of the reforms’ impact. According to empirical studies, the effect of competition on efficiency is unclear. The reforms have brought the most benefit to consumers, but their overall effect depends on many other factors. Competition for the market appeared to be a better way of implementing competition than competition in the market. In addition, comprehension of the problems complicating the analysis and their inclusion in the evaluation process constitutes an important point in evaluating the reforms’ effects and could be inspiring for countries that have not yet implemented all reforms.

In this paper, the authors present an EOQ model with substitutions between products and a dynamic inventory replenishment policy. Their key assumption is that many products in the market are substitutable at different levels, and that, in most cases, a customer who discovers that a desired product is unavailable will choose to consume a product with similar attributes or functionality, rather than not purchase at all. Therefore, given a firm that stocks multiple substitutable products, the authors assume that a stock out of one product has a direct impact on other products’ demand. The main purpose of our model is to enable inventory managers to develop ordering policies that ensures that, in the event that a specific product runs out and cannot be replenished due to unforeseen circumstances, the consequent increase in demand for related products will not cause further stock out incidents. To this end, the authors introduce a dependency factor, a variable that indicates the level of dependency, or correlation, between one product and another. The dependencies among the various products offered by the firm are embedded into the EOQ formula and assumptions, enabling managers to update their ordering schedules as needed. This approach has the potential to generate more practical and realistic purchasing and inventory optimization policies.

Background: Agriculture is a production system in which the economic principles of organisation act in mutual dependence with its ecological boundaries.

Background: The paper focusses on the efficiency evaluation of the EU-28 NUTS 2 regions production process according to the concept of the Regional Competitiveness Index 2013.

Background: Research in business failure and insolvency prediction provides numerous potential variables, which are in the position to differentiate between solvent and insolvent firms. Nevertheless, not all of them have the same discriminatory power, and therefore their general applicability as crisis indicators within early warning systems seems questionable. Objectives: The paper aims to demonstrate that gearing-ratio is not an appropriate predictor for firm failures/bankruptcies. Methods/Approach: The first and the second order derivatives for the gearing-ratio formula were computed and mathematically analysed. Based on these results an interpretation was given and the suitability of gearing-ratio as a discriminator within business failure prediction models was discussed. These theoretical findings were then empirically tested using financial figures from financial statements of Austrian companies for the observation period between 2008 and 2010. Results: The theoretical assumptions showed that gearing-ratio is not a suitable predictor for early warning systems. This finding was confirmed with empirical data. Conclusions: The inclusion of gearing-ratio within business failure prediction models is not able to provide early warning signals and should therefore be ignored in future model building attempts.

Shadow economy is an important part of economy in almost all countries especially the developing ones. Most of active firms in this part of economy have negative externality on the environment. Considering the importance of sustainable development and growing international pressures to maintain and support the environment more and more attentions have been drawn to the factors affecting and threatening environmental health. The present paper for the first time considers the role of variables like polity index and active population to total population ratio and how they affect the shadow economy. In addition to the main direct effects of these variables on shadow economy the indirect effects of causal variables through interaction with shadow economy are also examined. Since the relationship between shadow economy and air pollution has been somehow disregarded in economic literature to a large extent in Iran and to some extent at international level the present paper for the first time focuses on the relationship between shadow economy and air pollution. The results indicate that on average the ratio of shadow economy to GDP is 12.25% and a 1% increase in the size of the shadow economy raises the water pollution by 0.17%.

In this article, after defining a conceptional framework for defining and measuring shadow economy in Iran a close attention is also paid to a more precise definition of shadow economy itself. It is also tried to estimate it's changing process and size during 1975-2007 based on the new definition. Direct and indirect approaches are also briefly discussed to estimate the shadow economy. Then, the strengths and weaknesses of each method are pointed out. So far, almost all of the researches carried out in Iran regarding estimation of shadow economy have mainly focused on structural equation modeling approach using Lisrel. Here in this paper for the first time both structural equation modeling software programs of Amos Graphics and Lisrel are applied to estimate the shadow economy in Iran. A comparison of the process and output of both software packages is also done in this research. Finally, in addition to investigating the direct effects of the causal variables, the interactional effects of them on latent variable of the shadow economy are also analyzed.

Fluctuations in fiscal policy affect monetary policy and the central bank, because the government’s general budget is highly dependent on oil prices and its fluctuations. Therefore, this paper designs a New Keynesian model for Iran with nominal rigidities (prices and wages) and analyzes the impact of technology, oil price, government spending and money supply shocks on macroeconomic variables (inflation, output) in economy of Iran. The data in this article are related to the fixed prices in the year 2004 and run annually from 1966 to 2008 on a per capita basis. Having logarithms taken, the variables are de-traded through Hodrick - Prescott filter. The final model equations are linearized around the steady state and using Uhlig (1999) approach, accidental equations are also linearized and are specified as space state pattern in Matlab software. Finally, the calibration of parameters are assessed, variables are simulated and compared with real data. The results show that the recommended model can simulate the impact of shocks on macroeconomic variables. It also shows that inflation rises in response to all shocks except that of technology. As the figures show, it is also revealed that non-oil output increases in response to technology, oil price, government spending and money supply.

This paper develops a New Keynesian dynamic stochastic general equilibrium (DSDE) model to study Iran's economy. The model considers the dependence of Iran's economy to oil exports. Oil sector and oil export revenues have been modeled as a separate sector and one of the government budget resources, respectively. In this model, like in other New Keynesian DSGE models, firms face nominal rigidities and the intermediate-good sector is monopolistically competitive. Four shocks (productivity, oil revenues, money growth rate and government expenditure) have been introduced as the sources of volatility. The findings show that business cycle moments generated by the model and those of actual statistics from the economy are closely related. The model produces more volatile private investment and less volatile private consumption than non-oil output. Impulse response functions of shocks show that non-oil output increases in response to productivity, oil revenues, money growth rate and government expenditure shocks. Although non-oil output increases in response to government expenditures shocks, crowding- out effect of these expenditures causes output to decrease after some periods.