Forex-foreteller: A News Based Currency Predictor Fang Jin (fang8), Nathan Self (nwself), Parang Saraf (parang), Patrick Butler (pabutler), Wei Wang (tskatom)

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Presentation transcript:

Forex-foreteller: A News Based Currency Predictor Fang Jin (fang8), Nathan Self (nwself), Parang Saraf (parang), Patrick Butler (pabutler), Wei Wang (tskatom) & Naren Ramakrishnan (naren) Department of Computer Science, Virginia Tech

Introduction - Foreign Exchange Market Most liquid financial market in the world Average daily turnover was USD 3.98 trillion in April 2010 Growth of approximately 20% as compared to 2007 United States GDP is around USD trillion Operates 24 hours a day except on weekends Geographically Dispersed Traders include large banks, central banks, institutional investors, currency speculators, corporations, governments and retail investors A variety of factors effect exchange rate: Economic Factors Political Conditions Market Psychology

Related Work Fundamental Analysis Analyses economic health of a country Employment Reports Inflation Productivity Trade Growth Technical Analysis Mathematical Techniques like VAR, ARCH, GARCH etc Based on Past Trends of financial indicators Can’t rely on just one type. Have to use a combination of both the techniques

Our Approach Bloomberg News Interest Rates Inflation Unanticipated News Past Currency Values Past Stock Values Linear Regression Model Final Prediction FundamentalTechnical

System Framework

Language Modeling Different Types of News Latent Dirichlet Allocation Model to identify different topics Top 30 topics are Identified Out of 30 topics, manually identify topics of Interest List of Interesting topics

Topic Clustering Identify trending topics by tracking topic distribution movement over time

Sentiment Analysis Interest Rate Increase/Decrease Inflation Increase/Decrease Unanticipated News

Linear Regression Interest Rates Inflation Unanticipated News Past Currency Values Past Stock Values Linear Regression Model Final Prediction Where: Δ c is currency change Δ r is interest rate change Δ f is interest rate change Δ s is currency change Δ e is currency change β r, β f, β s, β e are respective weights

Off-line Components

Online Components Displays the generated alerts and associated Audit trails for user analysis

EMBERS Visualizer Link:

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