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Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana Cover

Predicting Stock Market Price Movement Using Sentiment Analysis: Evidence From Ghana

Open Access
|Jun 2020

References

  1. [1] A. E. Khedr, S. E. Salama, and N. Yaseen, “Predicting stock market behavior using data mining technique and news sentiment analysis,” International Journal of Intelligent Systems and Applications, vol. 9, no. 7, pp. 22–30, Jul. 2017. https://doi.org/10.5815/ijisa.2017.07.0310.5815/ijisa.2017.07.03
  2. [2] R. Ren, D. D. Wu, and T. Liu, “Forecasting stock market movement direction using sentiment analysis and support vector machine,” IEEE Systems Journal, vol. 13, no. 1, pp. 760–770, Mar. 2019. https://doi.org/10.1109/JSYST.2018.279446210.1109/JSYST.2018.2794462
  3. [3] V. S. Pagolu, K. N. Reddy, G. Panda, and B. Majhi, “Sentiment analysis of Twitter data for predicting stock market movements,” in 2016 International Conference on Signal Processing, Communication, Power and Embedded System, 2017, pp. 1345–1350. https://doi.org/10.1109/SCOPES.2016.795565910.1109/SCOPES.2016.7955659
  4. [4] F. Z. Xing, E. Cambria, and R. E. Welsch, “Intelligent asset allocation via market sentiment views,” IEEE Computational Intelligence Magazine, vol. 13, no. 4, pp. 25–34, Nov. 2018. https://doi.org/10.1109/MCI.2018.286672710.1109/MCI.2018.2866727
  5. [5] I. K. Nti, A. F. Adekoya, and B. A. Weyori, “A systematic review of fundamental and technical analysis of stock market predictions,” Artificial Intelligence Review, vol. 53, no. 4, pp. 3007–3057, Apr. 2020. https://doi.org/10.1007/s10462-019-09754-z10.1007/s10462-019-09754-z
  6. [6] A. Picasso, S. Merello, Y. Ma, L. Oneto, and E. Cambria, “Technical analysis and sentiment embeddings for market trend prediction,” Expert Systems with Applications, vol. 135, pp. 60–70, 2019. https://doi.org/10.1016/j.eswa.2019.06.01410.1016/j.eswa.2019.06.014
  7. [7] W. Chen, Y. Cai, K. Lai, and H. Xie, “A topic-based sentiment analysis model to predict stock market price movement using Weibo mood,” Web Intelligence, vol. 14, no. 4, pp. 287–300, 2016. https://doi.org/10.3233/WEB-16034510.3233/WEB-160345
  8. [8] B. Li, K. C. C. Chan, C. Ou, and S. Ruifeng, “Discovering public sentiment in social media for predicting stock movement of publicly listed companies,” Information Systems, vol. 69, pp. 81–92, Sep. 2017. https://doi.org/10.1016/j.is.2016.10.00110.1016/j.is.2016.10.001
  9. [9] K. Guo, Y. Sun, and X. Qian, “Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market,” Physica A: Statistical Mechanics and its Applications, vol. 469, pp. 390–396, 2017. https://doi.org/10.1016/j.physa.2016.11.11410.1016/j.physa.2016.11.114
  10. [10] A. Pathak and N. P. Shetty, “Indian stock market prediction using machine learning and sentiment analysis,” in 4th International Conference on Computational Intelligence in Data Mining, 2019, pp. 595–603. https://doi.org/10.1007/978-981-10-8055-5_5310.1007/978-981-10-8055-5_53
  11. [11] S. N. Balaji, P. V. Paul, and R. Saravanan, “Survey on sentiment analysis based stock prediction using big data analytics,” in 2017 Innovations in Power and Advanced Computing Technologies, 2017, pp. 1–5. https://doi.org/10.1109/IPACT.2017.824494310.1109/IPACT.2017.8244943
  12. [12] N. Metawa, M. K. Hassan, S. Metawa, and M. F. Safa, “Impact of behavioral factors on investors’ financial decisions: case of the Egyptian stock market,” International Journal of Islamic and Middle Eastern Finance and Management, vol. 12, no. 1, pp. 30–55, 2019. https://doi.org/10.1108/IMEFM-12-2017-033310.1108/IMEFM-12-2017-0333
  13. [13] Y. Ruan, A. Durresi, and L. Alfantoukh, “Using Twitter trust network for stock market analysis,” Knowledge-Based Systems, vol. 145, pp. 207–218, 2018. https://doi.org/10.1016/j.knosys.2018.01.01610.1016/j.knosys.2018.01.016
  14. [14] T. T. P. Souza and T. Aste, “Predicting future stock market structure by combining social and financial network information,” Physica A: Statistical Mechanics and its Applications, vol. 535, 122343, 2019. https://doi.org/10.1016/j.physa.2019.12234310.1016/j.physa.2019.122343
  15. [15] D. M. E. D. M. Hussein, “A survey on sentiment analysis challenges,” Journal of King Saud University - Engineering Sciences, vol. 30, no. 4, pp. 330–338, Oct. 2018. https://doi.org/10.1016/j.jksues.2016.04.00210.1016/j.jksues.2016.04.002
  16. [16] A. Bhardwaj, Y. Narayan, Vanraj, Pawan, and M. Dutta, “Sentiment analysis for Indian stock market prediction using Sensex and Nifty,” in 4th International Conference on Eco-friendly Computing and Communication Systems, 2015, pp. 85–91. https://doi.org/10.1016/j.procs.2015.10.04310.1016/j.procs.2015.10.043
  17. [17] G. Ranco, D. Aleksovski, G. Caldarelli, M. Grčar, and I. Mozetič, “The effects of Twitter sentiment on stock price returns,” PLoS ONE, vol. 10, no. 9, e0138441, 2015. https://doi.org/10.1371/journal.pone.013844110.1371/journal.pone.0138441457711326390434
  18. [18] N. Apergis and I. Pragidis, “Stock price reactions to wire news from the European Central Bank: Evidence from changes in the sentiment tone and international market indexes,” Inter. Adv. in Economic Research, vol. 25, no. 1, pp. 91–112, 2019. https://doi.org/10.1007/s11294-019-09721-y10.1007/s11294-019-09721-y
  19. [19] S. Poria, E. Cambria, and A. Gelbukh, “Aspect extraction for opinion mining with a deep convolutional neural network,” Knowledge-Based Systems, vol. 108, pp. 42–49, 2016. https://doi.org/10.1016/j.knosys.2016.06.00910.1016/j.knosys.2016.06.009
  20. [20] M. V. Mäntylä, D. Graziotin, and M. Kuutila, “The evolution of sentiment analysis—A review of research topics, venues, and top cited papers,” Computer Science Review, vol. 27, pp. 16–32, 2018. https://doi.org/10.1016/j.cosrev.2017.10.00210.1016/j.cosrev.2017.10.002
  21. [21] S. Merello, A. P. Ratto, L. Oneto, and E. Cambria, “Predicting Future Market Trends: Which Is the Optimal Window?” in INNS Big Data and Deep Learning Conference, 2020. https://doi.org/10.1007/978-3-030-16841-4_1910.1007/978-3-030-16841-4_19
  22. [22] R. Talib, K. M. Hanif, S. Ayesha, and F. Fatima, “Text mining: Techniques, applications and issues,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 11, pp. 414–418, 2016. https://doi.org/10.14569/IJACSA.2016.07115310.14569/IJACSA.2016.071153
  23. [23] Y. Wang, Q. Li, Z. Huang, and J. Li, “EAN: Event attention network for stock price trend prediction based on sentimental embedding,” in 10th ACM Conference on Web Science, 2019, pp. 311–320. https://doi.org/10.1145/3292522.332601410.1145/3292522.3326014
  24. [24] T. H. Nguyen, K. Shirai, and J. Velcin, “Sentiment analysis on social media for stock movement prediction,” Expert Systems with Applications, vol. 42, no. 24, pp. 9603–9611, 2015. https://doi.org/10.1016/j.eswa.2015.07.05210.1016/j.eswa.2015.07.052
  25. [25] S. Agarwal, S. Kumar, and U. Goel, “Stock market response to information diffusion through internet sources: A literature review,” International Journal of Information Management, vol. 45, pp. 118-131, Apr. 2019. https://doi.org/10.1016/j.ijinfomgt.2018.11.00210.1016/j.ijinfomgt.2018.11.002
  26. [26] T. Mitchell, Machine Learning, 1st Edition. McGraw Hill, 1997.
  27. [27] N. Kim, K. Lučivjanská, P. Molnár, R. Villa, “Google searches and stock market activity: Evidence from Norway,” Finance Research Letters, vol. 28, pp. 208–220, Mar. 2019. https://doi.org/10.1016/j.frl.2018.05.00310.1016/j.frl.2018.05.003
  28. [28] J. Ho and L. H. Kristiansen, “Can Google Trends predict gold returns and its implied volatility?” Master’s thesis, University of Stavanger, Norway, 2019.
  29. [29] X. Zhong and M. Raghib, “Revisiting the use of web search data for stock market movements,” Scientific Reports, vol. 9, 13511, 2019. https://doi.org/10.1038/s41598-019-50131-110.1038/s41598-019-50131-1675118331534170
  30. [30] J. Fang, G. Gozgor, C.-K. M. Lau, and Z. Lu, “The impact of Baidu index sentiment on the volatility of China’s stock markets,” Finance Research Letters, vol. 32, 101099, Jan. 2020. https://doi.org/10.1016/j.frl.2019.01.01110.1016/j.frl.2019.01.011
  31. [31] L. Bijl, G. Kringhaug, P. Molnar, and E. Sandvik, “Google searches and stock returns,” International Review of Financial Analysis, vol. 45, pp. 150–156, May 2016. https://doi.org/10.1016/j.irfa.2016.03.01510.1016/j.irfa.2016.03.015
  32. [32] R. Chiong, M. T. P. Adam, Z. Fan, B. Lutz, Z. Hu, and D. Neumann, “A sentiment analysis-based machine learning approach for financial market prediction via news disclosures,” in 2018 Genetic and Evolutionary Computation Conference Companion, 2018, pp. 278–279. https://doi.org/10.1145/3205651.320568210.1145/3205651.3205682
  33. [33] M. Kraus and S. Feuerriegel, “Decision support from financial disclosures with deep neural networks and transfer learning,” Decision Support Systems, vol. 104, pp. 38–48, Dec. 2017. https://doi.org/10.1016/j.dss.2017.10.00110.1016/j.dss.2017.10.001
  34. [34] A. García-Medina, L. Sandoval, E. U. Bañuelos, and A. M. Martínez-Argüello, “Correlations and flow of information between The New York Times and stock markets,” Physica A: Statistical Mechanics and its Applications, vol. 502, pp. 403-415, 2018. https://doi.org/10.1016/j.physa.2018.02.15410.1016/j.physa.2018.02.154
  35. [35] A. Alshahrani Hasan and A. C. Fong, “Sentiment analysis based fuzzy decision platform for the Saudi stock market,” in 2018 IEEE International Conference on Electro/Information Technology, 2018, pp. 23–29. https://doi.org/10.1109/EIT.2018.850029210.1109/EIT.2018.8500292
  36. [36] A. E. O. Carosia, G. P. Coelho, and A. E. A. Silva, “Analyzing the Brazilian financial market through Portuguese sentiment analysis in social media,” Applied Artificial Intelligence, vol. 34, no. 1, pp. 1–19, 2019. https://doi.org/10.1080/08839514.2019.167303710.1080/08839514.2019.1673037
  37. [37] K. M. Swamy, “Sentiment Analysis with Tensorflow – TensorFlow and Deep Learning Singapore,” 2017. [Online]. Available: https://engineers.sg/video/sentiment-analysis-with-tensorflowtensorflow-and-deep-learning-singapore--1742.
  38. [38] J. Roesslein, “Tweepy Documentation.” [Online]. Available: http://docs.tweepy.org/en/latest/.
  39. [39] R. Batra and S. M. Daudpota, “Integrating StockTwits with sentiment analysis for better prediction of stock price movement,” in 2018 International Conference on Computing, Mathematics and Engineering Technologies, 2018, pp. 1–5. https://doi.org/10.1109/ICOMET.2018.834638210.1109/ICOMET.2018.8346382
  40. [40] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media Inc., 2009.
  41. [41] J. Hogue and B. DeWilde, “Pytrends.” [Online]. Available: https://pypi.org/project/pytrends/.
  42. [42] B. Li and L. Han, “Distance weighted cosine similarity measure for text classification,” in 14th International Conference on Intelligent Data Engineering and Automated Learning, 2013, pp. 611–618. https://doi.org/10.1007/978-3-642-41278-3_7410.1007/978-3-642-41278-3_74
  43. [43] K. Ravi and V. Ravi, “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications,” Knowledge-Based Systems, vol. 89, pp. 14–46, Nov. 2015. https://doi.org/10.1016/j.knosys.2015.06.01510.1016/j.knosys.2015.06.015
  44. [44] S. Agrawal, D. Thakkar, D. Soni, K. Bhimani, and C. Patel, “Stock market prediction using machine learning techniques,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 5, no. 2, pp. 1099–1103, Mar.–Apr. 2019. https://doi.org/10.32628/CSEIT195229610.32628/CSEIT1952296
  45. [45] B. W. Wanjawa, “Predicting future Shanghai stock market price using ANN in the period 21-Sep-2016 to 11-Oct-2016,” 2016. [Online]. Available: https://arxiv.org/abs/1609.05394
  46. [46] F. Z. Xing, E. Cambria, and R. E. Welsch, “Natural language based financial forecasting: a survey,” Artificial Intelligence Review, vol. 50, no. 1, pp. 49–73, 2018. https://doi.org/10.1007/s10462-017-9588-910.1007/s10462-017-9588-9
  47. [47] S. Dey, Y. Kumar, S. Saha, and S. Basak, “Forecasting to classification: Predicting the direction of stock market price using xtreme gradient boosting,” 2016.
  48. [48] H. Z. Khan, S. T. Alin, and A. Hussain, “Price prediction of share market using artificial neural network (ANN),” International Journal of Computer Applications, vol. 22, no. 2, pp. 42–47, May 2011. https://doi.org/10.5120/2552-349710.5120/2552-3497
DOI: https://doi.org/10.2478/acss-2020-0004 | Journal eISSN: 2255-8691 | Journal ISSN: 2255-8683
Language: English
Page range: 33 - 42
Published on: Jun 5, 2020
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2020 Isaac Kofi Nti, Adebayo Felix Adekoya, Benjamin Asubam Weyori, published by Riga Technical University
This work is licensed under the Creative Commons Attribution 4.0 License.