Transcript A quantitative stock prediction system based on

國立雲林科技大學
National Yunlin University of Science and Technology
N.Y.U.S.T.
I. M.
A quantitative stock prediction
system based on financial news
Presenter : Chun-Jung Shih
Authors :Robert P. Schumaker , Hsinchun Chen
IPM 2009
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Intelligent Database Systems Lab
Outline
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Motivation
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Objective
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Methodology
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Experiments
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Conclusion
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Comments
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Motivation
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Predicting changes in the stock market has always had a
certain appeal to researchers.
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Acquiring relevant textual data is an important facet of
stock market prediction.
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Objective
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To create the Arizona Financial Text System (AZFinText)
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Seeks to contribute to the AZFinText system by comparing
AZFinText’s predictions against existing quantitative
funds and human stock pricing experts.
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Methodology
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Methodology
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Textual analysis
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To identify the Proper Nouns
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Use Arizona Text Extractor (AzTeK) system
Stock Quotations
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Gathers stock price data in 1 min increments
Model Building
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Provide superior performance to all combinations tested
Trading Experts
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Gathers the daily buy/sell recommendations from a variety of trading
experts
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Methodology
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Metrics
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Evaluates system output
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Closeness
Directional Accuracy
Simulated Trading
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Experiments
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Experiments
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Experiments
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Experiments
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Conclusion
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Sector had the best Directional Accuracy at 71.18% and
Simulated Trading of 8.50% return on investment.
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Sector also had the second-lowest Closeness score, 0.1954,
as compared to Universal, 0.0443.
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AZFinText had a Directional Accuracy of 71.18%, which
was second-best to DayTraders.com’s 81.82%.
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Comments
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Advantage
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Predicting changes in the stock market
Drawback
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N.Y.U.S.T.
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DayTraders.com’s Directional Accuracy batter than AZFinText
Application
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Information Retrieval
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