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Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Sohrab Mokhtari, Kang K. Yen, Jin Liu

Sohrab Mokhtari, Kang K Yen and Jin Liu. Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning. International Journal of Computer Applications 183(7):1-8, June 2021. BibTeX

	author = {Sohrab Mokhtari and Kang K. Yen and Jin Liu},
	title = {Effectiveness of Artificial Intelligence in Stock Market Prediction based on Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2021},
	volume = {183},
	number = {7},
	month = {Jun},
	year = {2021},
	issn = {0975-8887},
	pages = {1-8},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2021921347},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at the end of a business day based on the historical price data. In contrast, in the fundamental analysis, the classification ML algorithms are applied to classify the public sentiment based on news and social media. In the technical analysis, the historical price data is exploited from Yahoo Finance, and in fundamental analysis, public tweets on Twitter associated with the stock market are investigated to assess the impact of sentiments on the stock market


  1. Stock-market sentiment dataset.
  2. Alireza Abbaspour, Sohrab Mokhtari, Arman Sargolzaei, and Kang K Yen. A survey on active fault-tolerant control systems. Electronics, 9(9), 2020.
  3. Mohammad Abedin, Sohrab Mokhtari, and Armin B Mehrabi. Bridge damage detection using machine learning algorithms. In Health Monitoring of Structural and Biological Systems XV, volume 11593, page 115932P. International Society for Optics and Photonics, 2021.
  4. Girish Chandrashekar and Ferat Sahin. A survey on feature selection methods. Computers & Electrical Engineering, 40(1):16–28, 2014.
  5. Robert D Edwards, WHC Bassetti, and John Magee. Technical analysis of stock trends. CRC press, 2007.
  6. Riswan Efendi, Nureize Arbaiy, and Mustafa Mat Deris. A new procedure in stock market forecasting based on fuzzy random auto-regression time series model. Information Sciences, 441:113–132, 2018.
  7. Eugene F Fama. Efficient capital markets: Ii. The journal of finance, 46(5):1575–1617, 1991.
  8. Tom Howley, Michael G Madden, Marie-Louise O’Connell, and Alan G Ryder. The effect of principal component analysis on machine learning accuracy with high dimensional spectral data. In International Conference on Innovative Techniques and Applications of Artificial Intelligence, pages 209–222. Springer, 2005.
  9. Ahmad Kazem, Ebrahim Sharifi, Farookh Khadeer Hussain, Morteza Saberi, and Omar Khadeer Hussain. Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Applied soft computing, 13(2):947–958, 2013.
  10. Takashi Kimoto, Kazuo Asakawa, Morio Yoda, and Masakazu Takeoka. Stock market prediction system with modular neural networks. In 1990 IJCNN international joint conference on neural networks, pages 1–6. IEEE, 1990.
  11. From Charles D Kirkpatrick II and R Julie. Dow theory. CMT Level I 2019: An Introduction to Technical Analysis, page 15, 2019.
  12. AndrewWLo. The adaptive markets hypothesis. The Journal of Portfolio Management, 30(5):15–29, 2004.
  13. Caren Marzban. The roc curve and the area under it as performance measures. Weather and Forecasting, 19(6):1106–1114, 2004.
  14. Partha S Mohanram. Separating winners from losers among lowbook-to-market stocks using financial statement analysis. Review of accounting studies, 10(2-3):133–170, 2005.
  15. Sohrab Mokhtari, Alireza Abbaspour, Kang K. Yen, and Arman Sargolzaei. A machine learning approach for anomaly detection in industrial control systems based on measurement data. Electronics, 10(4), 2021.
  16. Sohrab Mokhtari and Kang K Yen. A novel bilateral fuzzy adaptive unscented kalman filter and its implementation to nonlinear systems with additive noise. In 2020 IEEE Industry Applications Society Annual Meeting, pages 1–6. IEEE, 2020.
  17. Sohrab Mokhtari and Kang K Yen. Impact of large-scale wind power penetration on incentive of individual investors, a supply function equilibrium approach. Electric Power Systems Research, 194:107014, 2021.
  18. Isaac Kofi Nti, Adebayo Felix Adekoya, and Benjamin Asubam Weyori. A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, pages 1–51, 2019.
  19. Jigar Patel, Sahil Shah, Priyank Thakkar, and Ketan Kotecha. Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert systems with applications, 42(1):259–268, 2015.
  20. Joseph D Piotroski. Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, pages 1–41, 2000.
  21. D Van Thanh, HN Minh, and DD Hieu. Building unconditional forecast model of stock market indexes using combined leading indicators and principal components: application to vietnamese stock market. Indian Journal of Science and Technology, 11, 2018.
  22. Haiqin Yang, Laiwan Chan, and Irwin King. Support vector machine regression for volatile stock market prediction. In International Conference on Intelligent Data Engineering and Automated Learning, pages 391–396. Springer, 2002.
  23. Xiao Zhong and David Enke. Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1):4, 2019.


Machine Learning, Time Series Prediction, Technical Analysis, Sentiment Embedding, Financial Market