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Study purpose. Existing approaches to forecasting dynamics of financial markets, as a rule, reduce to econometric calculations or technical analysis techniques, which in turn is a consequence of preferences among specialists, engaged in theoretical research and professional market participants, respectively. The main study purpose is developing a predictive economic-mathematical model that allows combining both approaches. In other words, this model should be estimated using traditional methods of econometrics and, at the same time, take into account the impact on the pricing process of the effect of clustering participants on behavioral patterns, as the basis of technical analysis. In addition, it is necessary that the created economic-mathematical model should take into account the phenomenon of existing historical trading levels and control the influence they exert on price dynamics, when it falls into local areas of these levels. Such analysis of price behavior patterns in certain areas of historical repeating levels is a popular approach among professional market participants. Besides, an important criterion of developing model’s potential applicability by a wide range of the interested specialists is its general functional form’s simplicity and, in particular, its components.Materials and methods. In the study, the market of the pound sterling exchange rate against the US dollar (GBP/USD) for the whole period of 2017 was chosen as the considered financial series, in order to forecast it. The presented economic-mathematical model was estimated by classical Kalman filter with an embedded neural network. The choice of these assessment tools can be explained by their wide capabilities in dealing with non-stationary, noisy financial market time series. In addition, applying Kalman filter is a popular technique for estimation local-level models, which principle was implemented in the newly model, proposed in article.Results. Using chosen approach of simultaneous applying Kalman filter and artificial neural network, there were obtained statistically significant estimations of all model’s coefficients. The subsequent model application on GBP/USD series from the test dataset allowed demonstrating its high predictive ability comparing with added random walk model, in particular judging by percentage of correct forecast directions. All received results have confirmed that constructed model allows effectively taking into account structural features of considered market and building good forecasts of future price dynamics.Conclusion. The study was focused on developing and improving apparatus of forecasting financial market prices dynamics. In turn, economic-mathematical model presented in that paper can be used both by specialists, carrying out theoretical studies of pricing process in financial markets, and by professional market participants, forecasting the direction of future price movements. High percentage of correct forecast directions makes it possible to use proposed model independently or as a confirmatory tool.

This material attempts to analyze the existing economic and mathematical models for taking into account the impact of the existing idea of corruption and the impact on its real situation. The paper describes a wide range of economically important situations the presence of contradictions in the national legislation, ineffective mechanisms of law enforcement and the judicial system, the lack of examination of administrative decisions, etc. The author generalizes diverse situations regarding the behavior of companies that compete with each other to obtain a contract, and the corrupt official acts as the monopoly. The article summarizes a variety of situations concerning the behavior of enterprises competing with each other in order to obtain a contract. The article describes the basic model of corruption the neoclassical mathematical model proposed by Argentinean scholar Susan Rose-Ackerman, in which corruption is considered in the context of “external” risks. This model is based on the principle of simulating economic relations in the triad: “customer – agent – client”.

Purpose: the main goal of the article is to investigate the possibilities of improving the procedures for organizing the optimal admission of students to higher education institutions that train modern specialists. In view of the fact that creative managers and entrepreneurs are vitally important for the national economy of Russia and its trade and economic partners, ready to assume managerial responsibility and entrepreneurial risks in turbulent economic conditions. Higher education in the global value system is regarded as the most significant factor in the economic and social development of national cultures and is realized in the conditions of severe competition in the educational market. Methods: the following basic research methods were used to solve the tasks: comparative analysis, scientific classification, systematization, theoretical generalization, and statistical methods. Results: scientific and methodical support of rational procedures for organizing the optimal reception of students with the greatest economic effect and level of education on the basis of the economic-mathematical model and the principles of multi-criteria choice was developed. Conclusions and Relevance: the subsystem of paid education in the field of higher education has led to the intensification of comprehensive studies to improve the efficiency of entrepreneurial activity, especially at the stage of preparation for admission of applicants for the forthcoming academic year to state budget and non-state educational institutions.

The article discusses financial crisis prediction models developed for Ukrainian insurance companies by using conditional probability models, namely the logistic regression. The Superdiscretization procedure for the bankrupt insurers has been implemented in order to enhance the classification characteristics of the models. The paper introduces a criterion for selecting the most applicable logistic model.