Using Words from Daily News Headlines to Predict the Movement of Stock Market Indices
Stock 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...
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| Format: | Article |
| Language: | English |
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University of Primorska
2017-06-01
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| Series: | Managing Global Transitions |
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| Online Access: | http://www.hippocampus.si/ISSN/1854-6935/15.109-121.pdf |
| Summary: | Stock 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. In this paper,
we propose 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. We use data from two sources (for
the past eight years): the daily data for the Dow Jones Industrial Average
index (‘open’ and ‘close’ values for each trading day) and the headlines of
the most voted 25 news on the Reddit World News Channel for the previous
‘trading days.’ By applying machine learning algorithms on these data
and analysing individual words that appear in the final predictive models,
we find that the words gay, propaganda and massacre are typically associated
with a daily increase of the stock index, while the word IRAN mostly
coincide with its decrease. While this work presents a first step towards
qualitative analysis of stock market models, there is still plenty of room for
improvements. |
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| ISSN: | 1581-6311 1854-6935 |