Advertisement

Evaluating the Influence of Twitter on the Saudi Arabian Stock Market Indicators

  • Mohammed Alshahrani
  • Fuxi Zhu
  • Ahmed Sameh
  • Lin Zheng
  • Summaya Mumtaz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 753)

Abstract

Investors critically analyze past pricing history, which influences their future investment decisions. Social media and news items have a significant impact on stock market indices. In this paper, we apply machine learning and NLP principles to find the correlations between Arabic sentiments and trends in the Saudi Arabian stock market, TADAWUL. More than 277K Arabic tweets were crawled and 114K tweets were annotated manually. Three types of correlations were implemented, Pearson’s correlation coefficient, Kendall rank correlation and Spearman’s rank correlation. Moreover, the paper illustrates that the most influential users could be predictable in the future, who can have a significant impact on the stock market trends. The first achievement of this study is the collection of the largest Arabic tweets dataset specialized in finance, which will be available to the public as soon as the annotation process is finished. The second achievement is that this is the first paper to study the influence of Twitter on the Saudi stock market using different types of correlation coefficients and investigated the role of mentions on the market trends.

Keywords

Twitter Stock market Sentiment analysis Correlations 

Notes

Acknowledgments

This research is supported by The National Natural Science Foundation of China with Grant No: 61272277.

We would also like to thank RIC at PSU for their support.

References

  1. 1.
    Maia, M., Almeida, J., Almeida, V.: Identifying user behavior in online social networks. In: Proceedings of the 1st Workshop on Social Network Systems, pp. 1–6 (2008)Google Scholar
  2. 2.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)Google Scholar
  3. 3.
    Bao, Y., Quan, C., Wang, L., Ren, F.: The Role of Pre-processing in Twitter Sentiment Analysis, pp. 615–624. Springer, Cham, (2014)Google Scholar
  4. 4.
    Chen, Y.-J., Chen, Y.-M., Lu, C.L.: Enhancement of stock market forecasting using an improved fundamental analysis-based approach. Springer, Heidelberg (2016)Google Scholar
  5. 5.
    Oliveira, N., Cortez, P., Areal, N.: On the Predictability of Stock Market Behavior Using StockTwits Sentiment and Posting Volume. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Ho, K.-Y., (Walter) Wang, W.: Predicting Stock Price Movements with News Sentiment: An Artificial Neural Network Approach. Springer International Publishing Switzerland (2016)Google Scholar
  7. 7.
    Qasem, M., Thulasiram, R., Thulasiram, P.: Twitter sentiment classification using machine learning techniques for stock markets. In: International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2015)Google Scholar
  8. 8.
    Cha, M., Benevenuto, F., Haddadi, H., Gummadi, P.K.: The world of connections and information flow in Twitter. IEEE Trans. Syst. Man Cybern. Part A 42(4), 991–998 (2012)Google Scholar
  9. 9.
    Abdul-Mageed, M., Diab, M.: AWATIF: a multi-genre corpus for modern standard Arabic subjectivity and sentiment analysis. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey. European Language Resources Association (ELRA) (2012)Google Scholar
  10. 10.
    Al-Sabbagh, R., Girju, R.: YADAC: yet another dialectal Arabic corpus. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey. European Language Resources Association (ELRA) (2012)Google Scholar
  11. 11.
    Refaee, E., Rieser, V.: Subjectivity and sentiment analysis of arabic twitter feeds with limited resources. In 9th International Conference on Language Resources and Evaluation (LREC’14), (2014)Google Scholar
  12. 12.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)Google Scholar
  13. 13.
    Aciar, S., Zhang, D., Simoff, S., et al.: Informed recommender: basing recommendations on consumer product reviews. IEEE Intell. Syst. 22(3), 39–47 (2007)CrossRefGoogle Scholar
  14. 14.
    Kang, H., Yoo, S.J., Han, D.: Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Syst. Appl. 39(5), 6000–6010 (2012)CrossRefGoogle Scholar
  15. 15.
    Atsalakis, G.S., Valavanis, K.P.: Surveying stock market forecasting techniques - part II: soft computing methods. Expert Syst. Appl. 36(3), 5932–5941 (2009)CrossRefGoogle Scholar
  16. 16.
    Silva, E., Castilho, D., Pereira, A., Brando, H.: A neural network-based approach to support the market making strategies in high-frequency trading. In: Proceedings of the International Joint Conference on Neural Networks, pp. 845–852 (2014)Google Scholar
  17. 17.
    Girijia, V.A., Manohara, P.M.M., Radhika, M.P., Aparna, K.: Stock Market Prediction: A Big Data Approach. IEEE (2015)Google Scholar
  18. 18.
    Fama, E.F., et al.: The adjustment of stock prices to new information. Int. Econ. Rev. 10(1), 1–21 (1969)CrossRefGoogle Scholar
  19. 19.
    Korayem, M., Crandall, D., Abdul-Mageed, M.: Subjectivity and Sentiment Analysis of Arabic: A Survey, pp. 128–139. Springe, Heidelberg (2012)Google Scholar
  20. 20.
    Rushdi-Saleh, M., Martín-Valdivia, M.T., Ureña-López, L.A., Perea-Ortega, J.M.: OCA: opinion corpus for Arabic. J. Am. Soc. Inform. Sci. Technol. 62(10), 2045–2054 (2011)CrossRefGoogle Scholar
  21. 21.
    Elarnaoty, M., AbdelRahman, S., Fahmy, A.: A Machine Learning Approach For Opinion Holder Extraction Arabic Language. CoRR, abs/1206.1011 (2012)Google Scholar
  22. 22.
    Elhawary, M., Elfeky, M.: Mining Arabic business reviews. In: Proceedings of International Conference on Data Mining Workshops (ICDMW), pp. 1108–1113. IEEE (2010)Google Scholar
  23. 23.
    El-Halees, A.: Arabic opinion mining using combined classification approach. In: Proceedings of the International Arab Conference on Information Technology, ACIT (2011)Google Scholar
  24. 24.
    Ibrahim, H.S., Abdou, S.M., Gheith, M.: MIKA: a tagged corpus for modem standard Arabic and colloquial sentiment analysis. In: The 2nd IEEE International Conference on Recent Trends in Information Systems (ReTIS) (2015)Google Scholar
  25. 25.
    Refaee, E., Rieser, V.: An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis (2015)Google Scholar
  26. 26.
    Nabil, M., Aly, M., Atiya, A.F.: ASTD: Arabic sentiment tweets dataset. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, 17–21 September 2015, pp. 2515–2519 (2015)Google Scholar
  27. 27.
    Duwairi, R.M., Marji, R., Sha’ban, N., Rushaidat, S.: Sentiment analysis in Arabic tweets. In: The 5th International Conference on Information and Communication Systems (ICICS) (2014)Google Scholar
  28. 28.
    Huang, Y., Zhou, S., Huang, K., Guan, J.: Boosting Financial Trend Prediction with Twitter Mood Based on Selective Hidden Markov Models, pp. 435–451. Springer, Cham (2015)Google Scholar
  29. 29.
    Idvall, P., Jonsson, C.: Algorithmic trading: hidden markov models on foreign exchange data. Master’s thesis, Sodertorn University (2008)Google Scholar
  30. 30.
    Li, X., Wang, C., Dong, J., Wang, F., Deng, X., Zhu, S.: Improving stock market prediction by integrating both market news and stock prices. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part II. LNCS, vol. 6861, pp. 279–293. Springer, Heidelberg (2011)Google Scholar
  31. 31.
    Pidan, D., El-Yaniv, R.: Selective prediction of financial trends with hidden markov models. In: Advances in Neural Information Processing Systems, pp. 855–863 (2011)Google Scholar
  32. 32.
    Zhang, Y.: Prediction of financial time series with Hidden Markov Models. Master’s thesis, Simon Fraser University (2004)Google Scholar
  33. 33.
    Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRefGoogle Scholar
  34. 34.
    Mittal, A., Goel, A.: Stock prediction using twitter sentiment analysis. Technical report, Stanford UniversityGoogle Scholar
  35. 35.
    Si, J., Mukherjee, A., Liu, B., Li, Q., Li, H., Deng, X.: Exploiting topic-based twitter sentiment for stock prediction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (vol. 2, Short Papers), pp. 24–29 (2013)Google Scholar
  36. 36.
    Sprenger, T.O., Tumasjan, A., Sandner, P.G., Welpe, I.M.: Tweets and trades: the information content of stock microblogs. European Financial Management (2013)Google Scholar
  37. 37.
    Wu, D., Ke, Y., Yu, J.X., Yu, P.S., Chen, L.: Detecting leaders from correlated time series. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5981, pp. 352–367. Springer, Heidelberg (2010)Google Scholar
  38. 38.
    Yang, B., Guo, C., Jensen, C.S.: Travel cost inference from sparse, spatio temporally correlated time series using markov models. Proc. VLDB Endow. 6(9), 769–780 (2013)CrossRefGoogle Scholar
  39. 39.
    Ahmad, K., Cheng, D., Almas, Y.: Multilingual sentiment analysis of financial news streams. In: Proceedings of the 1st International Conference on Grid in Finance, Palermo, pp. 1–8 (2006)Google Scholar
  40. 40.
    Almas, Y., Ahmad, K.: A note on extracting ‘sentiments’ in financial news in English, Arabic & Urdu. In: The 2nd Workshop on Computational Approaches to Arabic Script-Based Languages, Linguistic Soc America 2007 linguistic Institute, Stanford University, Stanford, California, Linguistic Society of America, pp. I–12 (2007)Google Scholar
  41. 41.
    AL-Rubaiee, H., Qiu, R., Li, D.: Identifying Mubasher Software Products through Sentiment Analysis of Arabic Tweets, Crown (2016). 978-1-4673-8743-9/16/Google Scholar
  42. 42.
    AL-Rubaiee, H., Qiu, R., Li, D.: Analysis of the relationship between Saudi twitter posts and the Saudi stock market. In: IEEE Seventh International Conference on Intelligent Computing and Information Systems, ICICIS 2015 (2015)Google Scholar
  43. 43.
    Twitter. The Search API (2016). https://dev.twitter.com/rest/public/. Accessed 15 June 2016
  44. 44.
    Bobko, P.: Correlation and Regression: Applications for Industrial Organizational Psychology and Management, 2nd edn. Sage Publications, Thousan Oaks (2001)CrossRefGoogle Scholar
  45. 45.
    Chen, P.Y., Popovich, P.: Correlation: Parametric and Non-parametric Measures. Sage Publications, Thousand Oaks (2002)CrossRefGoogle Scholar
  46. 46.
    Kendall, M.G., Gibbons, J.D.: Rank Correlation Methods, 5th edn. Edward Arnold, London (1990)zbMATHGoogle Scholar
  47. 47.
    Hazewinkel, M. (ed.): Linear Interpolation. Encyclopedia of Mathematics, Springer (2001). ISBN 978-1-55608-010-4Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mohammed Alshahrani
    • 1
    • 2
  • Fuxi Zhu
    • 1
  • Ahmed Sameh
    • 3
  • Lin Zheng
    • 1
  • Summaya Mumtaz
    • 4
  1. 1.Computer SchoolWuhan UniversityWuhanChina
  2. 2.College of Computer Science and ITAlbaha UniversityAlbahaSaudi Arabia
  3. 3.Computer CollegePrince Sultan UniversityRiyadhSaudi Arabia
  4. 4.Department of InformaticsUniversity of OsloOsloNorway

Personalised recommendations