Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

Call For Paper : Vol 11, Issue 03, March 2024

Call for Paper

Vol 11, Issue 03, March 2024

Indexing

Machine Learning Algorithms for Prediction of Blue Chip Stocks

Author : Rajvir Kaur 1 Anurag Sharma 2

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Date of Publication :31st December 2021

Abstract: Because of its unpredictable and non-linear character, accurate stock market prediction is a difficult and time-consuming undertaking for researchers. Several studies are based on previous data to determine the future worth of the stock market. However, external variables such as social media and news headlines now have a significant impact on the stock market. This study is based on predicting future stock prices using Twitter social media and news data, as well as historical data, to achieve high prediction outcomes. Machine learning techniques are employed in this article to forecast future stock values of five blue-chip corporations from various sectors. To construct the final dataset, the data was collected from many sources, including Twitter, news, and historical data, and then examined. Then, using the final dataset, we deployed three machine learning algorithms – SVM, Logistic regression, and Random forest – to forecast the model's accuracy and compared the algorithms to determine the best algorithm for stock market prediction. The results demonstrate that logistic regression earned the highest prediction accuracy for five blue-chip businesses, ranging from 85 percent to 89 percent, and a comparative analysis shows that logistic regression performed better than SVM and Random forest algorithms

Reference :

    1. A. Sharma, D. Bhuriya, and U. Singh, “Survey of stock market prediction using machine learning approach,” Proc. Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2017, vol. 2017-Janua, pp. 506–509, 2017,.
    2. S. Kompella and K. C. Chilukuri, “STOCK MARKET PREDICTION USING MACHINE LEARNING METHODS,” Int. J. Comput. Eng. Technol., vol. 10, no. 3, pp. 20–30, 2019.
    3.  W. Khan et al., “Predicting stock market trends using machine learning algorithms via public sentiment and political situation analysis,” Soft Comput., vol. 0123456789, 2019,.
    4. D. Selvamuthu, V. Kumar, and A. Mishra, “Indian stock market prediction using artificial neural networks on tick data,” Financ. Innov., vol. 5, no. 1, 2019,.
    5. “Machine learning can help | Machine learning for stock market |
    6. S. Jyothirmayee, V. D. Kumar, C. S. Rao, and R. S. Shankar, “Predicting Stock Exchange using Supervised Learning Algorithms,” Int. J. Innov. Technol. Explor. Eng., vol. 9, no. 1, pp. 4081–4090, 2019,.
    7. B. M. Pagolu, Venkata Sasank, Kamal Nayan Reddy, Ganapati Panda, “Sentiment Analysis of Twitter Data for Predicting Stock Market Movements,” Int. Conf. Signal Process. Commun. Power Embed. Syst., pp. 1345–1350, 2016.
    8. S. Kalra and J. S. Prasad, “Efficacy of News Sentiment for Stock Market Prediction,” Proc. Int. Conf. Mach. Learn. Big Data, Cloud Parallel Comput. Trends, Prespectives Prospect. Com. 2019, pp. 491–496, 2019,
    9.  A. Nayak, M. M. M. Pai, and R. M. Pai, “Prediction Models for Indian Stock Market,” Procedia Comput. Sci., vol. 89, pp. 441– 449, 2016,.
    10. K. Sai and R. Vanukuru, “Stock Market Prediction Using Machine Learning,” Int. Res. J. Eng. Technol., vol. 5, no. 10, pp. 1032–1035, 2018,
    11. R. Choudhry and K. Garg, “A Hybrid Machine Learning System for Stock Market Forecasting,” World Acad. Sci. Eng. Technol., no. July, pp. 315–318, 2008.
    12. K. H. Sadia, A. Sharma, A. Paul, and S. Sanyal, “Stock Market Prediction Using Machine Learning Algorithms,” Int. J. Eng. Adv. Technol., vol. 8, no. 4, pp. 25–31, 2019
    13. S. S. A. A. Usmani, Mehak, Syed Hasan Adil, Kamran Raza, “Stock Market Prediction Using Machine Learning Techniques,” Int. Conf. Comput. Inf. Sci., pp. 322–327, 2016.
    14.  A. Porshnev, I. Redkin, and A. Shevchenko, “Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis .,” Int. Conf. Data Min. Work., pp. 440–444, 2013,.
    15. M. Vijh, D. Chandola, V. A. Tikkiwal, and A. Kumar, “Stock Closing Price Prediction using Machine Learning Techniques,” Procedia Comput. Sci., vol. 167, no. 2019, pp. 599–606, 2020,.
    16. A. Rajeev and D. Harshitha, “StockGuru : Smart Way to Predict Stock Price Using Machine Learning,” no. 4, pp. 48– 52, 2021.
    17. I. Parmar et al., “Stock Market Prediction Using Machine Learning,” ICSCCC 2018 - 1st Int. Conf. Secur. Cyber Comput. Commun., pp. 574–576, 2018,.
    18. O. Hegazy, O. S. Soliman, and M. A. Salam, “A Machine Learning Model for Stock Market Prediction,” Int. J. Comput. Sci. Telecommun., vol. 4, no. December, pp. 17–23, 2014.
    19. T. K. Lee, J. H. Cho, D. S. Kwon, and S. Y. Sohn, “Global stock market investment strategies based on financial network indicators using machine learning techniques,” Expert Syst. Appl., vol. 117, pp. 228–242, 2019,. 
    20. “Sentiment Analysis | Lexalytics.” sentiment-analysis. Rule-Based Sentiment Analysis in Python for Data Scientists.” ule-based-sentiment-analysis-in-python
    21.  A. Naresh and P. Venkata Krishna, “An efficient approach for sentiment analysis using machine learning algorithm,” Evol. Intell., vol. 14, no. 2, pp. 725–731, 2021,..

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