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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
Email: [email protected]
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 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
Related Work
• Fundamental Analysis
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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
Fundamental
Technical
Bloomberg News
Interest Rates
Inflation
Past Currency Values
Unanticipated News
Linear Regression Model
Final Prediction
Past Stock Values
System Framework
Language Modeling
Latent Dirichlet
Allocation Model
to identify different
topics
Different Types of News
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
Inflation Increase/Decrease
Interest Rate Increase/Decrease
Sentiment Analysis
Unanticipated News
Linear Regression
Interest Rates
Inflation
Unanticipated News
Past Currency Values
Past Stock Values
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
Off-line Components
Online Components
Displays the generated alerts and associated Audit trails for user analysis
EMBERS Visualizer
Link: http://embers.cs.vt.edu/embers/alerts/visualizer_fin?layout=grid
Other EMBERS Products
Civil Unrest Predictor
Influenza Like Illness Predictor
Rare Diseases Predictor
Ablation Visualizer
Link: http://embers.cs.vt.edu/embers/alerts/visualizer_fin?layout=grid