tiffany iwantoro_exchange rate directional forecasting using sentiment analysis on social media in...
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Final PresentationTuesday, August 24th, 2015
Presented byTiffany Iwantoro - 19012057
SupervisorDeddy Priatmodjo Koesrindartoto, PhD
Tutorial 1E SBM ITB10 AM – 11 AM
ISBN 978-0-9942714-2-6
Social Media Trends in Indonesia
Source: eMarketers, Forbes
- User-friendly- Accessible- Low-cost- Show real time event
Social Media Exposure
Exchange rateSupply
Demand
• Self-fulfilling prophecy• Behavioral finance
Fluctuation of Exchange Rate
Source: Yahoo! Finance
USD/IDR
Aug 1st, 201411.760
May 31st, 201513.219
12,40%
Fluctuation of Exchange Rate
Source: Yahoo! Finance
EUR/IDR
Aug 1st, 201415.794
May 31st, 201514.494
8,96%
Fluctuation of Exchange Rate
Source: Yahoo! Finance
JPY/IDR
Aug 1st, 2014114,6051
May 31st, 2015106,4546
7,66%
Government Loans
Corporate Loans
Government Balance of Payment
Export and Import Forex Trading Investor or
Individual
Uses of Foreign Currencies
1.2.1 Is there any relations between market sentiments and exchange rate?
1.2.2 How significant is the relations between market sentiments and exchange rate?
Problem Identification
Research Objectives
1.2.1 To find out the relations between market sentiments and exchange rate
1.2.2 To find out the significance between market sentiments and exchange rate
Previous Findings
Research Objectives
Lucia Russo (2013)
• “Can Social Micro-blogging be Used to Forecast Intraday Exchange Rate?”
• Time series analysis
• Can predict in short run
Fang Jin (2012)
• “Forex Foreteller: Currency Trend Modelling using News Articles”
• Other factor : inflation, interest rate
K.S. Madhava Rao (2013)
• “Exchange Rate Market Sentiment Analysis of Major Global Currencies”
• EUR, GBP, SRD, YEN, ZAR/USD
• Time series and probabilistic mode
Alexander Kurov (2008)
• “Investor Sentiment, Trading Behavior, and Informational Efficiency in Index Future Markets”
• Using intraday frequency trading
Vasilios P. (2007)
• “Market Sentiment and Exchange Rate Directional Forecasting”
• Efficient Market Hypothesis in Weak Form
• EUR, GBP, AUD/USD
• Indonesian Rupiah (IDR)• Major global currencies (USD, EUR, JPY)• Twitter in Indonesia
• Sentiment in Indonesian language• Linear regression• Semantria
Research Contributions
Research Design
Problem Identification
Research Objectives
Literature Study Data Collection
Sentiment Dictionary
Classical Assumption Test
Linear Regression
Conclusion and Recommendation
Journals
Textbook
Sentiment Analysis
Exchange Rate
Tweets
Exchange Rate
• USD/IDR• EUR/IDR• JPY/IDR
Jan 1st, 2015 – May 31st, 2015
Tweets
Data CollectionFilteringKeywords
• USD IDR• Dollar rupiah
• EUR IDR• Euro rupiah
• JPY IDR• Yen rupiah
EUR/IDR JPY/IDRUSD/IDR10,247tweets
2,314tweets
1,545tweets
Problem Identification
Research Objectives
Literature Study Data Collection
Sentiment Dictionary
Classical Assumption Test
Linear Regression
Conclusion and Recommendation
Journals
Textbook
Sentiment Analysis
Exchange Rate
Tweets
Research Design
Sentiment Dictionary
Score Description
+1- Indicates positive sentiment- High buying or selling interest towards current exchange rate- Potentially influence others to buy or sell currencies
0- Neutral sentiment towards exchange rate- Author aims only to spread news/information
-1- Indicates negative sentiment- Low buying or selling interest towards current exchange rate- Potentially influence others not to buy or sell currencies
Indonesian Word English Translation Sentiment Score
Menguat Strengthened +1
Naik Up, rise, increase, escalate +1
Perkasa Strong +1
Penguatan Strengthening, reinforcement +1
Kuat Strong +1
Positif Positive +1
Melemah Weakened, fall off -1
Turun Down, sink, subside -1
Anjlok Drop -1
Pelemahan Weakening, impairment -1
Tertekan Oppressed -1
Ambruk Collapse, crumble, tumbling -1
Terpuruk Worsen -1
Stabil Stable 0
Bertahan Survive, last, withstand 0
Tetap Still, consistently, constant 0
Problem Identification
Research Objectives
Literature Study Data Collection
Sentiment Dictionary
Classical Assumption Test
Linear Regression
Conclusion and Recommendation
Journals
Textbook
Sentiment Analysis
Exchange Rate
Tweets
Research Design
Sentiment Analysis
0.51 until 1 Positive-0.5 until 0.5 Neutral-0.51 until -1 Negative
𝑹=𝑷𝒕−𝑷𝒕−𝟏
𝑷𝒕−𝟏 Average sentimentper day
Problem Identification
Research Objectives
Literature Study Data Collection
Sentiment Dictionary
Classical Assumption Test
Linear Regression
Conclusion and Recommendation
Journals
Textbook
Sentiment Analysis
Exchange Rate
Tweets
Research Design
Classical Assumption Test
USD/IDR Classical Assumption Test
EUR/IDR Classical Assumption Test
JPY/IDR Classical Assumption Test
USD/IDR EUR/IDR JPY/IDR
Sample 129 151 151
Normality Test(Kolmogorov-Smirnov Test)
Asymp. Sig (2-tailed)
0.509 0.121 0.167
Heteroscedasticity Test Sig. Score 0.271 0.425 0.831
Auto Correlation Test(Durbin Watson)
DW Score 2.206 1.986 1.954
Result Pass Pass Pass
Classical Assumption Test Summary
Problem Identification
Research Objectives
Literature Study Data Collection
Sentiment Dictionary
Classical Assumption Test
Linear Regression
Conclusion and Recommendation
Journals
Textbook
Sentiment Analysis
Exchange Rate
Tweets
Research Design
Linear Regression
USD/IDR Linear Regression
EUR/IDR Linear Regression
JPY/IDR Linear Regression
Linear Regression Summary
USD/IDR EUR/IDR JPY/IDR
Sample 129 151 151
R 0.433 0.361 0.318
R Square 0.188 0.131 0.101
F-stat 29.314 22.368 16.740
Regression model Y = 0.005 X Y = 0.011X Y = 0.006X
T-stat 5.414 4.730 4.092
Sig. 0.000 0.000 0.000
Result H0 rejected H0 rejected H0 rejected
H0 = there are no significant relations between market sentiment and exchange rateH1 = there are significant relations between market sentiment and exchange rate
Conclusion
1. The are significant relations between market sentiment and exchange rate in Indonesia.
2. Indonesian’s Twitter can show significant relations between market sentiment and exchange rate as much as
USD/IDR EUR/IDR JPY/IDR18,8% 13,1% 10,1%
Recommendation
• Various types of social media such as Facebook, blog, websites, and discussion groups
• Online news• Time-series analysis• Intra-day trading data for the tweet and the currency rate• Broaden the sentiment words into local language and social media
language instead of formal language in Indonesia.