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Contribution Details

Type Master's Thesis
Scope Discipline-based scholarship
Title Forecasting Financial Time Series Based on Sentiment Analysis
Organization Unit
Authors
  • Ming Deng
Supervisors
  • Erich Walter Farkas
Language
  • English
Institution University of Zurich
Faculty Faculty of Business, Economics and Informatics
Number of Pages 46
Date 2016
Abstract Text Previous research uses positive and negative word-lists to quantitatively measure the general tone of financial articles and study the effect of these sentiment variables on financial time series. This paper introduces a novelty feature construction method of financial documents based on natural language processing techniques and appraisal theory from a psychological point of view. I find that uncertainty emotion expressed in news articles can reflect the stock trading volume of individual firms. Besides, sentiment features like surprise and uncertainty have significant influence on cumulative abnormal returns during a period after the earning announcement day. These features give valuable information of the post-earnings-announcement drift by associate the drift direction with emotions expressed in financial articles. Keywords: Financial Textual Analysis, Post-Earnings-Announcement Drift, Appraisal Theory, Natural Language Processing, Sentiment Analysis.
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