Application of ensemble of recurrent neural networks for forecasting of stock market sentiments

Abstract

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.