Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis

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Author(s)

Anthony R. Calingo 1,* Ariel M. Sison 1 Bartolome T. Tanguilig III 1

1. Technological Institute of the Philippines, Quezon City, 1109, Philippines

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2016.10.02

Received: 14 Feb. 2016 / Revised: 4 May 2016 / Accepted: 22 Jul. 2016 / Published: 8 Oct. 2016

Index Terms

Social media, sentiment analysis, causality, data mining, stock market

Abstract

Stock market prediction has been an interesting research topic for many years. Finding an efficient and effective means of predicting the stock market found its way in different social networking platforms such as Twitter. Studies have shown that public moods and sentiments can affect one's opinion. This study explored the tweets of the Filipino public and its possible effects on the movement of the closing Index of the Philippine Stock Exchange. Sentiment Analysis was used in processing individual tweets and determining its polarity - either positive or negative. Tweets were given a positive and negative probability scores depending on the features that matched the trained classifier. Granger causality testing identified whether or not the past values of the Twitter time series were useful in predicting the future price of the PSE Index. Two prediction models were created based on the p-values and regression algorithms. The results suggested that the tweets collected using geo location and local news sources proved to be causative of the future values of the Philippine Stock Exchange closing Index.

Cite This Paper

Anthony R. Caliñgo, Ariel M. Sison, Bartolome T. Tanguilig III, "Prediction Model of the Stock Market Index Using Twitter Sentiment Analysis", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.10, pp.11-21, 2016. DOI:10.5815/ijitcs.2016.10.02

Reference

[1]Hong, H., Kubik, J. D., and Stein, J. C. (2004). Social Interaction and Stock-Market Participation. The Journal of Finance, 59(1), 137-163. doi: 10.3386/w8358

[2]Bollen, J., Mao, H., and Zeng, X. (2011). Twitter Mood Predicts the Stock Market. Journal of Computational Science, 2(1), 1-8. doi: 10.1016/j.jocs.2010.12.007

[3]Chen, H., De, P., Hu, Y. J., and Hwang, B. H. (2014). Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media. Review of Financial Studies, 27(5), 1367-1403. doi: 10.1093/rfs/hhu001

[4]Dondio, P. (2013). Stock Market Prediction Without Sentiment Analysis: Using a Web-Traffic Based Classifier and User-Level Analysis. In System Sciences (HICSS), 2013 46th Hawaii International Conference. pp. 3137-3146. IEEE. doi: 10.1109/hicss.2013.498

[5]Yu, S., and Kak, S. (2012). A Survey of Prediction Using Social Media. arXiv preprint arXiv:1203.1647. unpublished.

[6]Oh, C., and Sheng, O. (2011). Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement. 32nd ICIS Association for Information Systems. 17.

[7]Oliveira, N., Cortez, P., and Areal, N. (2013). Some Experiments on Modeling Stock Market Behavior Using Investor Sentiment Analysis and Posting Volume from Twitter. In Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics. p. 31. ACM. doi: 10.1145/2479787.2479811

[8]Mayfield, A. (2008). What is Social Media?. iCrossing E-book. Retrieved from http://ebooksoneverything.com/marketing/WhatisSocialMedia.pdf

[9]Kwak, H., Lee, C., Park, H., and Moon, S. (2010). What is Twitter, a Social Network or a News Media?. In Proceedings of the 19th international conference on World wide web. pp. 591-600. ACM. doi: 10.1145/1772690.1772751

[10]Asur, S., and Huberman, B. A. (2010). Predicting the Future with Social Media. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference. Vol. 1, pp. 492-499. IEEE. doi: 10.1109/wi-iat.2010.63

[11]Tumasjan, A., Sprenger, T. O., Sandner, P. G., and Welpe, I. M. (2010). Predicting Elections with Twitter: What 140 Characters Reveal About Political Sentiment. ICWSM, 10, 178-185. doi: 10.2139/ssrn.1833192

[12]Bermingham, A., and Smeaton, A. F. (2011). On Using Twitter to Monitor Political Sentiment and Predict Election Results. Sentiment Analysis where AI meets Psychology (SAAIP) Workshop at the International Joint Conference for Natural Language Processing (IJCNLP). 2-4.

[13]Bollen, J., Mao, H., and Pepe, A. (2011). Modeling Public Mood and Emotion: Twitter Sentiment and Socio-Economic Phenomena. ICWSM, 11, 450-453.

[14]Mittal, A., and Goel, A. (2012). Stock Prediction Using Twitter Sentiment Analysis. Standford University, CS229 2011. Retrieved from: http://cs229. stanford. edu/proj2011/GoelMittal-StockMarketPredictionUsing TwitterSentimentAnalysis. pdf.

[15]Zhang, X., Fuehres, H., and Gloor, P. A. (2011). Predicting Stock Market Indicators Through Twitter “I hope it is not as bad as I fear”. Procedia-Social and Behavioral Sciences, 26, 55-62. doi: 10.1016/j.sbspro.2011.10.562

[16]Rao, T., and Srivastava, S. (2012). Analyzing Stock Market Movements Using Twitter Sentiment Analysis. In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012) pp. 119-123. IEEE Computer Society.

[17]Ding, T., Fang, V., and Zuo, D. (2013). Stock Market Prediction based on Time Series Data and Market Sentiment. Retrieved from: http://murphy.wot.eecs.northwestern.edu/~pzu918/EECS349/final_dZuo_tDing_vFang.pdf

[18]Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis lectures on human language technologies, 5(1), 1-167. doi: 10.1007/978-3-642-19460-3_11

[19]Granger, C. W. (1969). Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica: Journal of the Econometric Society, 424-438. doi: 10.2307/1912791

[20]Mao, H., Counts, S., and Bollen, J. (2011). Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data. arXiv preprint arXiv:1112.1051. unpublished.

[21]Bahadori, M. T., and Liu, Y. (2013). An Examination of Practical Granger Causality Inference. In Proceedings of the 2013 SIAM International Conference on Data Mining. doi: 10.1137/1.9781611972832.52

[22]Liew, V. K. S. (2004). Which lag length selection criteria should we employ?. Economics bulletin, 3(33), 1-9.

[23]Pang, B., Lee, L., and Vaithyanathan, S. (2002). Thumbs up?: Sentiment Classification using Machine Learning Techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing. Association for Computational Linguistics. Vol.10, 79-86.  doi: 10.3115/1118693.1118704

[24]Zaki, M.J. and Meira, W. Jr. (2014). Data Mining and Analysis. Fundamental Concepts and Algorithms. New York, New York: Cambridge University Press.