Economists typically make simplifying assumptions to make the solution and estimation of their highly complex models feasible. These simplifications include approximating the true nonlinear dynamics of the model, disregarding aggregate uncertainty or assuming that all agents are identical. While...

Fast technological changes and constant growth of knowledge in many areas have led to an increasing importance of different approach to education. Efficient education is the foundation of modern society and it has the most important role in preparing students for a very flexible labour market....

Financial indicators are the most used variables in measuring the business performance of companies, signaling about the financial position, comprehensive income, and other significant reporting aspects. In a competitive environment, the performance measurement model allows performing...

Purpose of the article: The paper is focused on the forecast of stock markets of the Central European countries, known as V4, by means of soft computing. The tested model is constructed by a combination of fuzzy logic and artificial neural networks. A total of four SAX, PX, BUX, WIG stock...

Central bank intervention in the form of quantitative easing (QE) during times of low interest rates is a controversial topic. This paper introduces a novel approach to study the effectiveness of such unconventional measures. Using U.S. data on six key financial and macroeconomic variables...

The objective of the article is to develop and test in practice a mechanism for constructing AI/ML-based predictions, adapted for use in the system of government socio-economic administration in Ukraine. Research design is represented by several methods like qualitative analysis in order to...

Die Prognose der Insolvenzgefährdung von Unternehmen anhand statistischer Methodik war und ist eine bedeutende Aufgabe empirischer Forschung. Eine Möglichkeit der Beurteilung der finanziellen bzw. wirtschaftlichen Verfassung von Unternehmen stellt die sog. externe Bilanzanalyse anhand...

In recent years support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving...

Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns Abstract: Motivated by the recurrent Neural Networks, this paper proposes a recurrent Support Vector Regression (SVR) procedure to forecast nonlinear ARMA model based simulated data...