A quantitative stock prediction system based on financial news Presenter : Chun-Jung Shih Authors :Robert P. Schumaker , Hsinchun Chen 國立雲林科技大學 National Yunlin University of Science and Technology IPM 2009
Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments
Motivation • Predicting changes in the stock market has always had a certain appeal to researchers. • Acquiring relevant textual data is an important facet of stock market prediction.
Objective • To create the Arizona Financial Text System (AZFinText) • Seeks to contribute to the AZFinText system by comparing AZFinText’s predictions against existing quantitative funds and human stock pricing experts. 2317鴻海
Methodology • Textual analysis • To identify the Proper Nouns • Use Arizona Text Extractor (AzTeK) system • Stock Quotations • Gathers stock price data in 1 min increments • Model Building • Provide superior performance to all combinations tested • Trading Experts • Gathers the daily buy/sell recommendations from a variety of trading experts
Methodology • Metrics • Evaluates system output • Closeness • Directional Accuracy • Simulated Trading
Conclusion • Sector had the best Directional Accuracy at 71.18% and Simulated Trading of 8.50% return on investment. • Sector also had the second-lowest Closeness score, 0.1954, as compared to Universal, 0.0443. • AZFinText had a Directional Accuracy of 71.18%, which was second-best to DayTraders.com’s 81.82%. 12
Comments • Advantage • Predicting changes in the stock market • Drawback • DayTraders.com’sDirectionalAccuracybatterthanAZFinText • Application • Information Retrieval 13