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

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[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.2017.07.03Search in Google Scholar

[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.2018.2794462Search in Google Scholar

[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.2016.7955659Search in Google Scholar

[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.2018.2866727Search in Google Scholar

[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-zSearch in Google Scholar

[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.2019.06.014Search in Google Scholar

[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-160345Search in Google Scholar

[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.2016.10.001Search in Google Scholar

[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.2016.11.114Search in Google Scholar

[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_53Search in Google Scholar

[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.2017.8244943Search in Google Scholar

[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-12-2017-0333Search in Google Scholar

[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.2018.01.016Search in Google Scholar

[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.2019.122343Search in Google Scholar

[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.2016.04.002Search in Google Scholar

[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.2015.10.043Search in Google Scholar

[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.0138441457711326390434Search in Google Scholar

[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-ySearch in Google Scholar

[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.2016.06.009Search in Google Scholar

[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.2017.10.002Search in Google Scholar

[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_19Search in Google Scholar

[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.2016.071153Search in Google Scholar

[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/10.1145/3292522.332601410.1145/3292522.3326014Search in Google Scholar

[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.2015.07.052Search in Google Scholar

[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.2018.11.002Search in Google Scholar

[26] T. Mitchell, Machine Learning, 1st Edition. McGraw Hill, 1997.Search in Google Scholar

[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.2018.05.003Search in Google Scholar

[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.Search in Google Scholar

[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/s41598-019-50131-1675118331534170Search in Google Scholar

[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.2019.01.011Search in Google Scholar

[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.2016.03.015Search in Google Scholar

[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/10.1145/3205651.320568210.1145/3205651.3205682Search in Google Scholar

[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.2017.10.001Search in Google Scholar

[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.2018.02.154Search in Google Scholar

[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.2018.8500292Search in Google Scholar

[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/10.1080/08839514.2019.167303710.1080/08839514.2019.1673037Search in Google Scholar

[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.Search in Google Scholar

[38] J. Roesslein, “Tweepy Documentation.” [Online]. Available: http://docs.tweepy.org/en/latest/.Search in Google Scholar

[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.2018.8346382Search in Google Scholar

[40] S. Bird, E. Klein, and E. Loper, Natural Language Processing with Python. O’Reilly Media Inc., 2009.Search in Google Scholar

[41] J. Hogue and B. DeWilde, “Pytrends.” [Online]. Available: https://pypi.org/project/pytrends/.Search in Google Scholar

[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_74Search in Google Scholar

[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.2015.06.015Search in Google Scholar

[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/CSEIT1952296Search in Google Scholar

[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/1609.05394Search in Google Scholar

[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/s10462-017-9588-9Search in Google Scholar

[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.Search in Google Scholar

[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/10.5120/2552-349710.5120/2552-3497Search in Google Scholar

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