The aim of this paper is to demonstrate the usefulness of supervised machine learning algorithms in predicting the profitability of Romanian companies applying International Financial Reporting Standards (IFRS), both by regression and classification methods.The algorithms used in this research are linear regression (LinR), logistic regression (LogR), decision tree (DT), random forest (RF), K-nearest neighbor (KNN), and multi-layer perceptron (MLP).The results showed that both methods can produce models with high accuracy in profitability prediction.Thus, for regression, the best estimates were generated by the MLP model, and for classification, by the RF model.These results can be used to obtain sustainable models for predicting economic and financial performance, with a major impact on the management decisions of companies.

The objective of this paper is to investigate the relationship between economic growth, CO2 emissions, trade openness index, as well as the renewable energy consumption in Eastern European countries.The panel ARDL approach is used to reveal the long-and short-run impact of the selected variables on GDP per capita.Stationarity tests reveal that variables are I(0) and I(1), panel cointegration test confirm the long-run equilibrium between the variables, which enables us the use of panel ARDL methodology.The model reveals that in the long run there is a significant relationship between the dependent variable and all the exogenous ones for the selected countries.The Dumitrescu Hurlin causality test confirms the feedback causality between economic growth and trade openness, renewable energy consumption, and economic growth and unilateral causality running from CO2 emissions to economic growth.

In this study, we aimed to assess the degree of business cycle synchronisation in five CEE countries that have not adopted the euro (Romania, Bulgaria, Hungary, Poland and the Czech Republic), from the perspective of investigating similar developments with the three most important countries of the monetary union (Germany, France and Italy), respectively, with the peripheral countries most affected by the sovereign debt crisis (Greece, Portugal and Spain).The results confirm that membership of the monetary union does not automatically reduce the potential for asymmetric shocks, as in the case of peripheral countries, and that CEE countries do not have a similar path of improving business cycle synchronisation, with Romania and Bulgaria being closer to the Greek experience.

This paper presents a dynamic Quality Cost Model capable of reconciling contributions of Juran's traditional Model with those of the Zero DefectsModel.Here, the cost functions depend on a company's level of conformance in each period and at which stage of the Maturity Grid a company is positioned.To determine the stage at which the company is positioned on Crosby's Maturity Grid, expert's opinions will be considered using fuzzy logic to incorporate Hidden Quality Costs in the failure costs calculated by the company.The model will introduce exponential regression (ER) using Induced Ordered Weighted Average (IOWA).The results show that the learning process present in the proposed model leads it to evolve over time to positions that bring to mind the Zero Defects model.However, as appreciated in the case study, it can also regress and introduce the organisation into a level of saturation where the Cost of Quality is optimised before reaching perfection.

The paper aims to shape the companies' production stocks using fuzzy neutrosophic numbers.The stock modeling using neutrosophic and triangular fuzzy numbers improves the company's financial performance, new indicators being proposed, such as: the optimal quantity of stocks, the cost minimization function, the cost of placing order, and the cost of storing a purchased unit of product.The significant advantage of shaping stocks management using fuzzy neutrosophic numbers is that it allows companies to determine the optimal quantities of stocks to be supplied using nonlinear mathematical programming algorithms.The innovative models for determining the optimal quantity of stocks are structured in two categories, namely: optimal stock supply models with fuzzy neutrosophic variables, with a single product, a constant demand, with out-of-stock, as well as models of optimal supply of stocks, with fuzzy neutrosophic variables, with several products, a constant demand and with out-of-stock.Both models use algorithms specific to nonlinear mathematical programming and provide a complete picture of the companies' stocks acquisition strategies needed for its operational activity.Finally, the results obtained from the modeling/simulation validate the operationalisation of the stock models presented and the modern foundation of companies' decisions on stock performance indicators.

In this paper, we offer a mathematical model for the construction and implementation of a set of models for analysing the stability of labour reproduction processes and model of saving energy, evaluating the parameters that determine the state of the system that describes the processes of labour reproduction, optimal management of these processes; methodical recommendations for making management decisions to ensure the stability of labour reproduction processes have been developed.

The main objective of this paper is to analyse the efficiency of the technology transfer process in the 27 EU member states in support of the strategies formulation to improve the efficiency of technology transfer processes.Therefore, a conceptual framework was proposed based on the efficiency of the RD&I system and technology transfer.Following this conceptual framework, two DEA models were applied to evaluate the efficiency of the two subprocesses (RD&I system and technology transfer).The results revealed some highly efficient DMUs, but also that a relatively large number of countries are inefficient in terms of one or both subprocesses of technology transfer.The article also presents a series of strategies that policy makers can adopt to directly or gradually improve technology transfer efficiency to reach countries with highly efficient transformation of human and financial resources allocated to RD&I into visible results in the economic sector.

This paper investigates the idiosyncratic volatility transmission effect between four stock markets of Visegrad group countries. The research process involves isolation of the global market noises from the empirical timeseries, the use of an innovative NAGARCH-NIG model for conditional volatility creation and risk transmission measurement via a robust quantile regressionmodel.We find that the highest volatility spillover effect exists between Prague, Warsaw and Budapest stock markets, whereas Bratislava stock market receives and transmits a very low level of volatilities towards other three markets, regarding both tranquil and crisis periods.The most likely reason for such findings is the fact that the Slovakian stock market has the lowest level of market capitalisation and the lowest average daily trading volumes.However, this makes the SAX index a very suitable investment instrument in combination with the other three indices, whereas the best combination is the SAX index with WIG.

The aim of the study is to estimate the economic growth within the Caspian countries (Azerbaijan, Iran, Kazakhstan, Turkmenistan, Russia, and Uzbekistan) in the period 1997-2018 by applying panel methodological approach. From this perspective, two econometric models were created in which we investigated the impact of the explanatory variables (i.e. Brent Crude Oil Price, Exports, Imports, Government Expenditure, Foreign Direct Investments, and CO2 Emissions) on economic growth expressed according to the level of GDP per capita in these oil-producing Caspian countries. The economic growth in the Caspian countries was measured and estimated using specified panel regression methods, such as: the Common Effects Model, the Fixed Effects Model, and the Random Effects Model. The obtained results showed that GDP per capita is positively and directly influenced, generating a 1.60% increase influenced the Exports, while 10% increase of CO2 Emissions determines approximately 1% decrease of GDP per capita in the analysed period. At the same time, by applying the Vector Error Correction model (VECM), our results showed that the variables are not cointegrated and the level of GDP per capita is not influenced by the co-movements of the explanatory variables in the long-run. Interestingly, the economic growth is not influenced by the Foreign Direct Investments (FDI), which is recommended and indicated for the six Caspian countries to diversify their export opportunities (especially oil and natural gas) in order to the energy security objectives.

Environmental performance, social responsibility, and corporate governance quality are aspects of ESG performance. This study examines the influence of ESG performance on the financial performance of 333 banks located in 53 countries in Europe, America, and Asia, before and during the Covid-19 pandemic (2019Covid-19 pandemic ( -2021)). Our model design allows us to establish causality relationships. The main factors and financial data are collected from the Refinitiv database. The findings indicate that the bank environmental performance in 2019 has a negative influence on the return on equity during 2020, and that no other ESG factors are significant. Social responsibility expenditures and initiatives in 2020 positively influenced bank profitability in 2021. Furthermore, East Asian banks have higher stock market returns and earnings per share determined by the quality of corporate governance in the previous year. The environmental performance of 2020 has a negative influence on earnings per share in 2021, but only for the sample in East Asia. Implications for the banking sector and investors are proposed.