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Forex-foreteller: A News Based Currency Predictor

Forex-foreteller: A News Based Currency Predictor. Fang Jin, Nathan Self, Parang Saraf, Patrick Butler, Wei Wang, Naren Ramakrishnan Department of Computer Science Virginia Tech Aug 13, 2013. EMBERS. Funded by Intelligent Advanced Research Projects Activity (IARPA)

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Forex-foreteller: A News Based Currency Predictor

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  1. Forex-foreteller: A News Based • Currency Predictor • Fang Jin, Nathan Self, Parang Saraf, • Patrick Butler, Wei Wang, Naren Ramakrishnan • Department of Computer Science • Virginia Tech • Aug 13, 2013

  2. EMBERS • Funded by Intelligent Advanced Research Projects Activity (IARPA) • Primarily Interested in making predictions about Latin American Countries • The primary prediction areas are as follows: • Civil Unrest Events • Influenza Like Illness Events • Rare Diseases Events • Elections • Financial Events

  3. 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 16.62 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

  4. 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

  5. Our Approach • Fundamental • Technical • Bloomberg News • Past Currency Values • Unanticipated News • Interest Rates • Inflation • Past Stock Values • Linear Regression Model • Final Prediction

  6. System Framework

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

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

  9. Sentiment Analysis • Inflation Increase/Decrease • Interest Rate Increase/Decrease • Sentiment Analysis • Unanticipated News

  10. Linear Regression • Past Stock Values • Inflation • Past Currency Values • Interest Rates • Unanticipated News • Final Prediction • Linear Regression Model • Where: • Δc is currency change • Δr is interest rate change • Δf is interest rate change • Δs is currency change • Δe is currency change • βr, βf, βs, βe are respective weights

  11. Off-line Components

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

  13. EMBERS Visualizer • Link: http://embers.cs.vt.edu/embers/alerts/visualizer_fin?layout=grid

  14. Other EMBERS Products • Civil Unrest Predictor • Influenza Like Illness Predictor • Ablation Visualizer • Rare Diseases Predictor • Link: http://embers.cs.vt.edu/embers/alerts/visualizer_fin?layout=grid

  15. Fang Jin: jfang8@cs.vt.edu Parang Saraf: parang@cs.vt.edu

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