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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Review Article

A Study on the Impact of Sentiment Analysis on Stock Market Prediction

Author(s): Kiran Dhanasekaren, Sri Teja Aluri, Neeraj Karthikeyan, Saravanan Hari Baskaran and Ramani Selvanambi*

Volume 16, Issue 1, 2023

Published on: 25 May, 2022

Article ID: e250522202256 Pages: 21

DOI: 10.2174/2666255815666220315153545

Price: $65

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Abstract

Background: Investors estimate how a company's stock or financial instrument will perform in the future, which is known as the stock market prediction. Stock markets are one of the many industries that have benefited substantially from the incredible breakthroughs in machine learning. To effectively estimate these markets, many researchers and companies are continually researching and developing various state-of-the-art approaches and algorithms.

Objective: The objective is to predict stock prices based on public sentiments. With a big collection of data from microblogging sites like Twitter, it is possible to analyse the thoughts or feelings of users on a wide scale. These sentiments play a major part in the way the stock market works. We review multiple papers and provide the advantages and disadvantages of various methods.

Methods: An in-depth examination of the most recent methodologies for predicting stock market values using sentiment analysis is offered, as well as the multiple consequences for stock markets when epidemics or major events occur.

Results: According to the findings, impact sentiment analysis has a significant part in predicting stock market price movement, allowing for greater profit.

Conclusion: With modern machine learning and deep learning processes, we can forecast stock costs with a few degrees of precision. This research examines how stock expectations have changed over time, as well as the most recent and effective technique for forecasting, supplying, and minimizing speculators' losses.

Keywords: Sentiment analysis, stock price prediction, social media sentiment, natural language processing, machine learning, stock market.

Graphical Abstract

[1]
W. Khan, U. Malik, M.A. Ghazanfar, M.A. Azam, K.H. Alyoubi, and A.S. Alfakeeh, "Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis", Soft Comput., pp. 1-25, 2019.
[2]
A. Derakhshan, and H. Beigy, "Sentiment analysis on stock social media for stock price movement prediction", Eng. Appl. Artif. Intell., vol. 85, pp. 569-578, 2019.
[http://dx.doi.org/10.1016/j.engappai.2019.07.002]
[3]
D. Shah, H. Isah, and F. Zulkernine, "Stock market analysis: A review and taxonomy of prediction techniques", Int. J. Fin. Stud., vol. 7, no. 2, p. 26, 2019.
[http://dx.doi.org/10.3390/ijfs7020026]
[4]
X. Zhong, and D. Enke, "Forecasting daily stock market return using dimensionality reduction", Expert Syst. Appl., vol. 67, pp. 126-139, 2017.
[http://dx.doi.org/10.1016/j.eswa.2016.09.027]
[5]
C.H. Park, and S.H. Irwin, "What do we know about the profitability of technical analysis?", J. Econ. Surv., vol. 21, no. 4, pp. 786-826, 2007.
[http://dx.doi.org/10.1111/j.1467-6419.2007.00519.x]
[6]
T.H. Nguyen, K. Shirai, and J. Velcin, "Sentiment analysis on social media for stock movement prediction", Expert Syst. Appl., vol. 42, no. 24, pp. 9603-9611, 2015.
[http://dx.doi.org/10.1016/j.eswa.2015.07.052]
[7]
J.S. Abarbanell, and B.J. Bushee, "Fundamental analysis, future earnings, and stock prices", J. Account. Res., vol. 35, no. 1, pp. 1-24, 1997.
[http://dx.doi.org/10.2307/2491464]
[8]
Y. Hu, K. Liu, X. Zhang, L. Su, E.W. Ngai, and M. Liu, "Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review", Appl. Soft Comput., vol. 36, pp. 534-551, 2015.
[http://dx.doi.org/10.1016/j.asoc.2015.07.008]
[9]
P.C. Tetlock, "Giving content to investor sentiment: The role of media in the stock market", J. Finance, vol. 62, no. 3, pp. 1139-1168, 2007.
[http://dx.doi.org/10.1111/j.1540-6261.2007.01232.x]
[10]
M. Mazboudi, and S. Khalil, "The attenuation effect of social media: Evidence from acquisitions by large firms", J. Financ. Stab., vol. 28, pp. 115-124, 2017.
[http://dx.doi.org/10.1016/j.jfs.2016.11.010]
[11]
E. Bartov, L. Faurel, and P.S. Mohanram, "Can Twitter help predict firm-level earnings and stock returns?", Account. Rev., vol. 93, no. 3, pp. 25-57, 2017.
[http://dx.doi.org/10.2308/accr-51865]
[12]
A. Picasso, S. Merello, Y. Ma, L. Oneto, and E. Cambria, "Technical analysis and sentiment embeddings for market trend prediction", Expert Syst. Appl., vol. 135, pp. 60-70, 2019.
[http://dx.doi.org/10.1016/j.eswa.2019.06.014]
[13]
A. Groß-Klußmann, S. König, and M. Ebner, "Buzzwords build momentum: Global financial twitter sentiment and the aggregate stock market", Expert Syst. Appl., vol. 136, pp. 171-186, 2019.
[14]
M.Y. Chen, and T.H. Chen, "Modeling public mood and emotion: Blog and news sentiment and socio-economic phenomena", Future Gener. Comput. Syst., vol. 96, pp. 692-699, 2019.
[http://dx.doi.org/10.1016/j.future.2017.10.028]
[15]
T.C. Huang, R.N. Zaeem, and K.S. Barber, "It is an equal failing to trust everybody and to trust nobody: Stock price prediction using trust filters and enhanced user sentiment on twitter", ACM Trans. Internet Technol., vol. 19, no. 4, pp. 1-20, 2019.
[http://dx.doi.org/10.1145/3338855]
[16]
M.Y. Chen, C.H. Liao, and R.P. Hsieh, "Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach", Comput. Human Behav., vol. 101, pp. 402-409, 2019.
[http://dx.doi.org/10.1016/j.chb.2019.03.021]
[17]
H. Maqsood, I. Mehmood, M. Maqsood, M. Yasir, S. Afzal, and F. Aadil, "A local and global event sentiment based efficient stock exchange forecasting using deep learning", Int. J. Inf. Manage., vol. 50, pp. 432-451, 2020.
[http://dx.doi.org/10.1016/j.ijinfomgt.2019.07.011]
[18]
D.C. Broadstock, and D. Zhang, "Social-media and intraday stock returns: The pricing power of sentiment", Finance Res. Lett., vol. 30, pp. 116-123, 2019.
[http://dx.doi.org/10.1016/j.frl.2019.03.030]
[19]
R. Ren, D.D. Wu, and T. Liu, "Forecasting stock market movement direction using sentiment analysis and support vector machine", IEEE Syst. J., vol. 13, no. 1, pp. 760-770, 2018.
[http://dx.doi.org/10.1109/JSYST.2018.2794462]
[20]
E. Frank, M. Hall, G. Holmes, R. Kirkby, B. Pfahringer, I.H. Witten, and L. Trigg, Weka-a machine learning workbench for data mining. Data Mining and Knowledge Discovery Handbook., Springer: Boston, MA, 2009, pp. 1269-1277.
[http://dx.doi.org/10.1007/978-0-387-09823-4_66]
[21]
R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, vol. 14. 1995, no. 2, pp. 1137-1145.
[22]
M. Kumar, and M. Thenmozhi, "Forecasting stock index movement: A comparison of support vector machines and random forest", Indian Institute of Capital Markets 9th Capital Markets Conference Literature, 2006.
[http://dx.doi.org/10.2139/ssrn.876544]
[23]
R. Choudhry, and K. Garg, "A hybrid machine learning system for stock market forecasting", World Acad. Sci. Eng. Technol., vol. 39, no. 3, pp. 315-318, 2008.
[24]
S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Landauer, and R. Harshman, "Indexing by latent semantic analysis", J. Am. Soc. Inf. Sci., vol. 41, no. 6, pp. 391-407, 1990.
[http://dx.doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9]
[25]
T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction., Springer Science & Business Media, 2009.
[http://dx.doi.org/10.1007/978-0-387-84858-7]
[26]
V. Vapnik, The Nature of Statistical Learning Theory., Springer science & business media, 2013.
[27]
R.N. Zaeem, D. Liau, and K.S. Barber, "Predicting disease outbreaks using social media: Finding trustworthy users", Proceedings of the Future Technologies Conference, 2018, pp. 369-384.
[28]
R.J. Shiller, "From efficient markets theory to behavioral finance", J. Econ. Perspect., vol. 17, no. 1, pp. 83-104, 2003.
[http://dx.doi.org/10.1257/089533003321164967]
[29]
P.B. Schiilkop, C. Burgest, and V. Vapnik, "Extracting support data for a given task", Proceedings, First International Conference on Knowledge Discovery & Data Mining, 1995, pp. 252-257.
[30]
Z. Xia, and J. Chen, "Mining the relationship between COVID-19 sentiment and market performance", arXiv, 2021.
[31]
M. Costola, M. Nofer, O. Hinz, and L. Pelizzon, "Machine learning sentiment analysis, Covid-19 news and stock market reactions", SAFE Working Paper, 2020.
[32]
C. Chou, J. Park, and E. Chou, "Predicting stock closing price after COVID-19 based on sentiment analysis and LSTM", In 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 5, pp. 2752-2756 IEEE, 2021
[33]
A. Gaur, "Sentiment analysis of major news announcements to predict the aggregate market indicators amid COVID-19 outbreak", Int. J. Manag, vol. 11, no. 10, pp. 1504-1513, 2020.
[34]
Z. Machmuddah, S.D. Utomo, E. Suhartono, S. Ali, and W. Ali Ghulam, "Stock market reaction to COVID-19: Evidence in customer goods sector with the implication for open innovation", J. Open Innov., vol. 6, no. 4, p. 99, 2020.
[http://dx.doi.org/10.3390/joitmc6040099]
[35]
C. Gondaliya, A. Patel, and T. Shah, "Sentiment analysis and prediction of Indian stock market amid Covid-19 pandemic", IOP Conf. Series Mater. Sci. Eng., vol. 1020, no. 1, p. 012023, 2021.
[IOP Publishing] [http://dx.doi.org/10.1088/1757-899X/1020/1/012023]

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