Textual Analysis of Stock Market Prediction Using Financial News Articles

This paper examines the role of financial news articles on three different textual representations; Bag of Words, Noun Phrases, and Named Entities and their ability to predict discrete number stock prices twenty minutes after an article release. Using a Support Vector Machine (SVM) derivative, we show that our model had a statistically significant impact on predicting future stock prices compared to linear regression. We further demonstrate that using a Noun Phrase representation scheme performs better than the de facto standard of Bag of Words.

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