LECTURER: Branko KAV\u0160EK,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 University of Primorska; Faculty of Mathematics,
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Natural Sciences and Information Technologies;
\n\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Jo\u0161ef Stefan Institute; Artificial Intelligence Laboratory<\/p>\n

TITLE: Using Words from Daily News Headlines to Predict the
\n\u00a0 \u00a0 \u00a0 \u00a0Movement of Stock Market Indices<\/p>\n

ABSTRACT:
\nStock market analysis is one of the biggest areas of interest for text mining. Many researchers proposed different approaches that use text information for predicting the movement of stock market indices. Many of these approaches focus either on maximising the predictive accuracy of the model or on devising alternative methods for model evaluation. On the\u00a0seminar, we will describe a more descriptive approach focusing on the models themselves, trying to identify the individual words in the text that most affect the movement of stock market indices. Data from two sources will be used: the daily data for the Dow Jones Industrial Average index (\u201copen\u201d and \u201cclose\u201d values for each trading day) and the headlines of the most voted 25 news on the Reddit WorldNews Channel for the previous \u201ctrading days\u201d (data has been gathered for the past 8 years). By applying machine learning algorithms on these data and analysing individual words we will show that certain words have a “positive” effect on stock indices while other words have a clearly “negative” effect.<\/p>\n