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Evaluating Sentiment in Financial News Articles: Working Paper Series--11-10

Schumaker, Robert P. and Zhang, Yulei and Huang, Chun-Neng and Chen, Hsinchun (2011) Evaluating Sentiment in Financial News Articles: Working Paper Series--11-10. Working Paper. NAU W.A. Franke College of Business.

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Abstract

We investigate the pairing of a financial news article prediction system, AZFinText, with sentiment analysis techniques. From our comparisons we found that news articles of a subjective nature were easier to predict in both price direction (59.0% vs 50.4% without sentiment) and through a simple trading engine (3.30% return vs 2.41% without sentiment). Looking into sentiment further, we found that news articles of a negative sentiment were easiest to predict in both price direction (50.9% vs 50.4% without sentiment) and our simple trading engine (3.04% return vs 2.41% without sentiment). Investigating the negative sentiment further, we found that AZFinText was best able to predict price decreases in articles of a positive sentiment (53.5%) and price increases in articles of a negative or neutral sentiment (52.4% and 49.5% respectively).

Item Type: Monograph (Working Paper)
Publisher’s Statement: Copyright, where appropriate, is held by the author.
ID number or DOI: 11-10
Keywords: Working paper, Knowledge management, prediction from textual documents, sentiment analysis
Subjects: H Social Sciences > HG Finance
NAU Depositing Author Academic Status: Faculty/Staff
Department/Unit: The W.A. Franke College of Business
Date Deposited: 17 Oct 2015 09:25
URI: http://openknowledge.nau.edu/id/eprint/1478

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