  The paper proposes a method of financial time series forecasting taking into
account the semantics of news. For the semantic analysis of financial news the
sampling of negative and positive words in economic sense was formed based on
Loughran McDonald Master Dictionary. The sampling included the words with high
frequency of occurrence in the news of financial markets. For single-root words
it has been left only common part that allows covering few words for one
request. Neural networks were chosen for modeling and forecasting. To automate
the process of extracting information from the economic news a script was
developed in the MATLAB Simulink programming environment, which is based on the
generated sampling of positive and negative words. Experimental studies with
different architectures of neural networks showed a high adequacy of
constructed models and confirmed the feasibility of using information from news
feeds to predict the stock prices.
