Browse > Article
http://dx.doi.org/10.9717/kmms.2016.19.7.1107

A Study on Efficient Market Hypothesis to Predict Exchange Rate Trends Using Sentiment Analysis of Twitter Data  

Komariah, Kokoy Siti (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Machbub, Carmadi (School of Electrical Engineering and Informatics, Institut Teknologi Bandung)
Prihatmanto, Ary S. (Center of ICT Research, Institut Teknologi Bandung)
Sin, Bong-Kee (Dept. of IT Convergence and Application Engineering, Pukyong National University)
Publication Information
Journal of Korea Multimedia Society / v.19, no.7, 2016 , pp. 1107-1115 More about this Journal
Abstract
Efficient Market Hypothesis (EMH), states that at any point in time in a liquid market security prices fully reflect all available information. This paper presents a study of proving the hypothesis through daily Twitter sentiments using the hybrid approach of the lexicon-based approach and the naïve Bayes classifier. In this research we analyze the currency exchange rate movement of Indonesia Rupiah vs US dollar as a way of testing the Efficient Market Hypothesis. In order to find a correlation between the prediction sentiments from Twitter data and the actual currency exchange rate trends we collect Twitter data every day and compute the overall sentiment to label them as positive or negative. Experimental results have shown 69% correct prediction of sentiment analysis and 65.7% correlation with positive sentiments. This implies that EMH is semi-strong Efficient Market Hypothesis, and that public information provide by Twitter sentiment correlate with changes in the exchange market trends.
Keywords
Efficient Market Hypothesis; Predicting Exchange Rate Trends; Twitter Sentiment Analysis; Hybrid Approach;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 S.G. Chowdhury, S. Routh, and S. Chakrabarti, "News Analytics and Sentiment Analysis to Predict Stock Price Trends," International Journal of Computer Science and Information Technologies, Vol. 5, No. 3, pp. 3595-3604, 2014.
2 J. Clarke, T. Jandik, and G. Mendelker, "The Efficient Markets Hypothesis," Expert Financial Planning: Advice from Industry Leaders, pp. 126-141, 2001.
3 E.F. Fama, "Efficient Capital Market: a Review of Theory and Empirical Work," Journal of Finance, Vol. 25, No. 2, pp. 383-417, 1970.   DOI
4 Indonesia's Weakening Rupiah Challenges Government, Available: http://www.bbc.com/news/business-32357063, (accessed Apr., 19, 2016).
5 P. Papaioannou, L. Russo, P. George, and C. I. Siettos, "Can Social Microblogging Be Used to Forecast Intraday Exchange Rates?," Journal Netnomics, Vol. 14, No. 1-2, pp. 47-68, 2013.   DOI
6 A. Montoyo, P.M. Barco, and A. Balahur, "Subjectivity and Sentiment Analysis: an Overview of the Current State of the Area and Envisaged Developments," Science Direct: Decision Support Systems, Vol. 53, No. 4, pp. 675-679, 2010.   DOI
7 M.S. Vohra and J.B. Teraiya, "A Comparative Study of Sentiment Analysis Techniques," Journal of Information, Knowledge and Research in Computer Engineering, Vol. 2, No. 2, pp. 313-317, 2012.
8 S. Kim and B.Y. Hwang, "Prospensity Analysis of Political Attitude of Twitter Users by Extracting Sentiment from Timeline," Journal of Korea Multimedia Society, Vol. 17, No. 1, pp. 43-51, 2014.   DOI
9 A. Mudinas, D. Zhang, and M. Levene, "Combining Lexicon and Learning based Approaches for Concept-level Sentiment Analysis," Proceedings of the First International Workshop on Issues of Sentiment Discovery and Opinion Mining, WISDOM '12, pp. 5:1-5:8, 2012.
10 F.A. Nielsen, "A New ANEW: Evaluation of a Word List for Sentiment Analysis in Microblogs," Proceedings of the ESWC2011 Workshop on 'Making Sense of Microposts': Big Things Come in Small Packages, Vol. 718, pp. 93-98, 2011.
11 B. Pang and L. Lee, "Opinion Mining and Sentiment Analysis," Foundations and Trends in Information Retrieval, Vol. 2, No. 1-2, pp. 1-135, 2008.   DOI
12 T.M. Mitchell, Machine Learning, McGraw-Hill, New York, 1997.
13 C.D. Manning and H. Schütze, Foundation of Statistical Natural Language Processing, Massachusetts: MIT Press, Cambridge, 1999.
14 Oanda, https://www.oanda.com/currency/historical-rates/, (accessed on Mar., 13, 2015).
15 Wikipedia: F1 Score, https://en.wikipedia.org/wiki/F1_score, (accessed on May, 22, 2016).
16 Wikipedia: Pearson Product-moment Correlation Coefficient, https://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient, (accessed on Mar., 15, 2015).