Expert methods, which widely applied for human decision making, were employed for neural networks. It was developed an exchange rates prediction and trading algorithm with using of experts information processing techniques -Delphi method and prediction compatibility. Proposed algorithm limited to eight experts. Each of experts represented recurrent neural network, Evolino-based Long Short-Term Memory (LSTM) by using of genetic learning algorithm, EVOlution of recurrent systems with LINear Outputs (EVOLINO). Statistical investigation of offered algorithm shows the significantly increase of the reliability of prediction. Developed algorithm was applied for trading of historical forex exchange rates. Obtained test trading results were presented

Use of artificial intelligence systems in forecasting financial markets requires a reliable and simple model that would ensure profitable growth. The model presented in the paper combines Evolino recurrent neural networks with orthogonal data inputs and the Delphi expert evaluation method for its investment portfolio decision making process. A statistical study demonstrates the reliability of the model and describes its accuracy. Capabilities of the model are demonstrated using a trading simulation.

Research background: Research and measurement of sentiments, and the integration of methods for sentiment analysis in forecasting models or trading strategies for financial markets are gaining increasing attention at present. The theories that claim it is difficult to predict the individual investor’s decision also claim that individual investors cause market instability due to their irrationality. The existing instability increases the need for scientific research.   Purpose of the article: This paper is dedicated to establishing a link between the individual investors’ behavior, which is expressed as sentiments, and the market dynamic, and is evaluated in the stock market. This article hypothesizes that the dynamics in the market is unequivocally related to the individual investor’s sentiments, and that this relationship occurs when the sentiments are expressed strongly and are unlimited. Methods: The research was carried out invoking the method of Evolino RNN-based prediction model. The data for the research from AAII (American Association of Individual Investors), an investor sentiment survey, were used. Stock indices and sentiments are forecasted separately before being combined as a single composition of distributions. Findings & Value added: The novelty of this paper is the prediction of sentiments of individual investors using an Evolino RNN-based prediction model. The results of this paper should be seen not only as the prediction of the connection and composition of investors’ sentiments and stock indices, but also as the research of the dynamic of individual investors’ sentiments and indices.

This paper aims at investigating military and demographic inter-linkages in the context of the Lithuanian sustainability. The investigation combines three important economic aspects such as demographic, military and sustainable development. The authors have revealed that demographic trends should be seen as a necessary conditions for ensuring the functioning of the military sector contributes to public security and sustainable development in general. Correlation and stepwise regression analysis, also Monte Carlo forecasting method have been applied for this purpose. Research results have revealed statistically significant interrelationship between military personnel as a share of total labour force and population growth rate, population median age, total fertility rate as well as birth rate. Moreover, Monte Carlo forecasting method allowed revealing for the next 10 years a steady slight increase in armed forces personnel, stable population growth rates, a rapid aging process and a slight decline of total fertility rate. Military and demographic estimations and future projections allow government to incorporate information into planning and sustainable development policy. The insights from this research may contribute to implementing the goals of sustainable development related to eradication of poverty, inequality, social exclusion, improvement in education, well-being and employment and tackling climate change.

Sustainable development is based on the idea of achieving an acceptable level of social, economic, and cultural development. However, there are a lot of impediments to achieving this idea so far. Population aging is one of these (and, probably, major) global trends affecting all countries and putting the realization of sustainable development goals at risk. The main goal of this article, therefore, is to test the relation between aging and three groups of indicators of sustainable development of EU countries. The investigation that forms the basis of the given article has focused on the median age of the population and six sustainable development indicators of the EU countries. The analysis covers annual data of the period from 2000 to 2018. All variables have been obtained from the Eurostat database. This has provided a possibility to compare the EU countries by indicators under consideration.