Textual Analysis of Stock Market Prediction Using Financial News Articles
Robert P. Schumaker and Hsinchun Chen Artificial Intelligence Lab, Department of Management Information Systems The University of Arizona, Tucson, Arizona 85721, USA {rschumak, hchen}@eller.arizona.edu
Abstract
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 Named Entities representation scheme performs better than the de facto standard of Bag of Words.
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Introduction
Stock Market prediction has always had a certain appeal for researchers. While
numerous scientific attempts have been made, no method has been discovered to accurately predict stock price movement. The difficulty of prediction lies in the complexities of modeling human behavior. Even with a lack of consistent prediction methods, there have been some mild successes. Stock Market research encapsulates two elemental trading philosophies; Fundamental and Technical approaches (Technical-Analysis 2005). In Fundamental analysis, Stock Market price movements are believed to derive from a security’s relative data. Fundamentalists use numeric information such as earnings, ratios, and management effectiveness to determine future forecasts. In Technical analysis, it is believed that market timing is key. Technicians utilize charts and modeling techniques to identify trends in price and volume. These later individuals rely on historical data in order to predict future outcomes. One area of limited success in Stock Market prediction comes from textual data. Information from quarterly reports or breaking news stories can... [continues]
Robert P. Schumaker and Hsinchun Chen Artificial Intelligence Lab, Department of Management Information Systems The University of Arizona, Tucson, Arizona 85721, USA {rschumak, hchen}@eller.arizona.edu
Abstract
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 Named Entities representation scheme performs better than the de facto standard of Bag of Words.
1
1
Introduction
Stock Market prediction has always had a certain appeal for researchers. While
numerous scientific attempts have been made, no method has been discovered to accurately predict stock price movement. The difficulty of prediction lies in the complexities of modeling human behavior. Even with a lack of consistent prediction methods, there have been some mild successes. Stock Market research encapsulates two elemental trading philosophies; Fundamental and Technical approaches (Technical-Analysis 2005). In Fundamental analysis, Stock Market price movements are believed to derive from a security’s relative data. Fundamentalists use numeric information such as earnings, ratios, and management effectiveness to determine future forecasts. In Technical analysis, it is believed that market timing is key. Technicians utilize charts and modeling techniques to identify trends in price and volume. These later individuals rely on historical data in order to predict future outcomes. One area of limited success in Stock Market prediction comes from textual data. Information from quarterly reports or breaking news stories can... [continues]
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