Data have shapes, and human intelligence and perception have to classify the forms of data to understand and interpret them. This article uses a sliding window technique and the main aim is to answer two questions. Is there an opportunity window in time series of stock exchange index? The second question is how to find a way to use the opportunity window if there is one. The authors defined the term opportunity window as a window that is generated in the sliding window technique and can be used for forecasting. In analysis, the study determined the different frequencies and explained how to evaluate opportunity windows embedded using time series data for the S&P 500, the DJIA, and the Russell 2000 indices. As a result, for the S&P 500 the last days of the patterns 0111, 1100, 0011; for the DJIA the last days of the patterns 0101, 1001, 0011; and finally for the Russell 2000, the last days of the patterns 0100, 1001, 1100 are opportunity windows for prediction.
The main purpose of the paper is an expert assessment of the relationship existing between selected indicators carried out using a relatively new tool in economic sciences: Fuzzy Cognitive Maps. The effect of its application is a graphical presentation of the relationship between the factors identified as the key ones. In the paper 23 indicators, describing four selected goals in the Strategy for Sustainable Development, 2030 Agenda were selected. It is assumed that the sustainable development goals should be related but according to the experts opinion this only applies to some indicators. This kind of relationships can be certainly identified in the case of the goals describing social and economic development, but often also economic and environmental development. However, the research results presented in the paper do not always confirm the existence of connections between individual indicators selected for the description of the goals of sustainable development. The paper tries to explain this problem.
The paper deals with an evaluation of the quality of services provided by healthcare organizations. First, an index representing a patient’s health condition is described, then its changes before and after being treated by a given entity are employed as a criterion to assess the operations of this entity. The index of a patient’s health condition is based on the theory of survival analysis, while a model of random effects is used to determine the quality of services based on health value added.
This paper presents a proposition to utilize flexible neural network architecture called Deep Hybrid Collaborative Filtering with Content (DHCF) as a product recommendation engine. Its main goal is to provide better shopping suggestions for customers on the e-commerce platform. The system was tested on 2018 Amazon Reviews Dataset, using repeated cross validation and compared with other approaches: collaborative filtering (CF) and deep collaborative filtering (DCF) in terms of mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). DCF and DHCF were proved to be significantly better than the CF. DHCF proved to be better than DCF in terms of MAE and MAPE, it also scored the best on separate test data. The significance of the differences was checked by means of a Friedman test, followed by post-hoc comparisons to control p-value. The experiment shows that DHCF can outperform other approaches considered in the study, with more robust scores.
A bank, particularly in developing countries like Turkey, is one of the most important institutions in the financial sector. Therefore knowing the factors affecting the performance of banks is important for the development of the sector. One of the factors affecting the risk and profitability of banking sector is the internal factors of the banks. The aim of this paper is to investigate the board of directors’ characteristics and its effect on risk level measured by non-performing loans and on bank performance measured by asset profitability using the Generalized Method of Moments (GMM) estimator. Data from nineteen deposit banks for the period 2012–2018 were used. The result of the study determined that the board size, foreign board members and the independent board members have an effect on both non-performing loans and the return on assets.
The aim of this paper is to empirically analyse the relationship between the trade wars and modes of transport for selected countries. For this purpose the causality relationship between trade value and sea transport / air transportation for EU–G20 and US–G20 countries was examined. Panel causality analysis was used as a method in the study. The empirical findings of the study show the existence of a causality relationship between the trade value and modes of transport (sea transport and air transport) for country groups. This shows that the countries’ sea and air transport will be adversely affected by trade wars.
The aim of the paper is to empirically estimate the growth-maximizing debt-to-GDP ratio in the case of Turkey. To calculate the growth-maximizing debt-to-GDP ratio FMOLS, DOLS, and CCR estimators are used for the period from 1960–2013. According to the empirical findings the growth-maximizing debt-to-GDP ratio varies between 34.3% and 38.7%. Based on a comparison of these ratios to current data (29.1% for 2018), Turkey has the capacity for additional borrowing to achieve a growth-maximizing debt-to-GDP ratio. If this additional borrowing capacity is used for public investment with a return greater than the interest cost of the additional debt economic growth will be maximized and public debt sustainability supported.
The aim of the article is to conduct an empirical analysis of the impact of aggregate and disaggregate private capital flows on economic growth in eleven MENA countries between 1980 and 2018. Unlike prior empirical studies, the fixed effect panel quantile approach developed by Canay (2011) is implemented. Findings suggest that there is a significant difference in the effects of private capital flows on economic growth across lower and higher quantiles. More specifically, the effects of total private capital flows, foreign direct investment flows, portfolio flows and debt flows are positive and statistically significant only for low and medium quantiles, indicating that the enhancing impact of private capital flows in terms of economic growth is only confirmed in countries with relatively low and medium growth rates. Moreover, debt flows affect economic growth in countries recording high growth rates, stressing the importance of financial development in routing those flows into the most productive projects in the economy.
As a result of previous multilateral negotiations tariff rates are generally low and cannot explain the reasons for recent proliferation of preferential trade agreements (PTAs). The aim of the paper is to look for other motivations of EU PTAs and to assess their importance for the EU. The main research methods are statistical, review and assessment of WTO documents and critical analysis of literature.
First, the present level of tariff protection on selected import markets was estimated. This level illustrates the scale of countries’ interest in their elimination of the existing tariffs. Also the share of preferential imports in the EU extra-trade was calculated and compared with trade on MFN basis. Next, reasons for PTAs were identified. The conclusions prove that 21st century PTAs are mainly motivated not by a reduction of tariffs but by the willingness to reduce the regulatory barriers (contained in rules on public procurement, environmental protection, etc.). The most dynamic trade nowadays involves flows of accessories and services. In this situation the importance of PTAs results from the fact that they serve as instruments eliminating national regulatory barriers faced by exporters of goods and resources on foreign markets. Thus PTAs support production and sales abroad. In the EU political motivations of PTAs are important as well.