Application of intelligent systems for news analytics

Abstract

The chapter starts with an overview of existing text mining systems whose main purpose is predicting equity price movements on the financial markets. In general, these systems transform the input text to a so-called sentiment score, a numerical value equivalent to the opinion of an analyst on the influence of the news text to the further development of the regarded stock. In the second part it is explored how the sentiment score relates to some of the relevant macroeconomic variables. It is suggested that raw sentiment score can be transformed to reveal sentiment reversals, and such transformed indicator relates better to future returns. As an example the project FINDS is presented as an integrated system that consists of a module that performs sentiment extraction from the financial news, a benchmark module for comparison between different classification engines, and a visualization module used for the representation of the sentiment data to the end users, thus supporting the traders in analysing news and making buy and sell decisions.