Abstract
Stock market prediction is the act of trying to determine the future value of a company's stock. The successful prediction of a stock's future price could yield significant profit. The main objective of this project is to predict the stock prices of any particular company using the foremost machine learning techniques. The machine learning model uses historical prices and human sentiments as two different inputs, and the output is distinguished as a graph showing the future prediction and a label (positive neutral and negative), respectively. The machine learning techniques used for prediction are the recurrent neural network (RNN), long short-term memory (LSTM) model and sentimental analysis. The machine learning model is then trained with several data points, and the results are evaluated. As for sentimental analysis, the public's opinion from a social media platform is scraped and then a label is generated.
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Reference
Malkiel, B.: A Random Walk Down Wall Street. W.W. Norton & Co., New York (1973)
Kale, A., Khanvilkar, O., Jivani, H., Kumkar, P., Madan, I., Sarode, T.: Forecasting Indian stock market using artificial neural networks: In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, pp. 1–5 (2018)
Deng, S., Mitsubuchi, T., Shioda, K., Shimada, T., Sakurai, A.: Combining technical analysis with sentiment analysis for stock price prediction. In: 2011 IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, Sydney, NSW, pp. 800–807 (2011)
Bhardwaj, S.: Which stocks fell the most due to corporate scandals in past one year? The Economic Times. M.economictimes.com (2020). [Online]. Available https://m.economictimes.com/wealth/invest/which-stocks-fell-the-most-due-to-corporate-scandals-in-past-one-year/amp_articleshow/72082456.cms. Accessed 27 April 2020
Zhao, Z., Rao, R., Tu, S., Shi, J.: Time-weighted LSTM model with redefined labeling for stock trend prediction. In: 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI), Boston, MA, pp. 1210–1217 (2017)
McNally, S., Roche, J., Caton, S.: Predicting the price of bitcoin using machine learning. In: 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, pp. 339–343 (2018)
Patel, J., Shah, S., Thakkar, P., Kotecha, K.: Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. In: Expert Systems with Applications, 05 Aug 2014. [Online]. Available https://www.sciencedirect.com/science/article/abs/pii/S0957417414004473. Accessed 29 April 2020
Oancea, B., Ciucu, Åž.C.: Time series forecasting using neural networks. arXiv.org, 07 Jan 2014 [Online]. Available https://arxiv.org/abs/1401.1333. Accessed 29 April 2020
Li, X, Xie, H., Chen, L., Wang, J., Deng, X.: News impact on stock price return via sentiment analysis. In: Knowledge-Based Systems, 26 April 2014. [Online]. Available https://www.sciencedirect.com/science/article/abs/pii/S0950705114001440. Accessed 29 April 2020
Zhang, Y., Wu, L.: Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network. In: Expert Systems with Applications, 28 Nov 2008.[Online].Available https://www.sciencedirect.com/science/article/abs/pii/S095741740800852X. Accessed 29 April 2020
Usmani, M., Adil, S.H., Raza, K., Ali, S.S.A.: Stock market prediction using machine learning techniques. In: 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), Kuala Lumpur, pp. 322–327 (2016)
Nandankumar, R., Uttamraj, K,R., Vishal, R., Lokeswari, Y.V.: Stock price prediction using long short term memory. IRJET (2018)
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Shaikh, A., Panuganti, A., Husain, M., Singh, P. (2021). Stock Market Prediction Using Machine Learning. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_42
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DOI: https://doi.org/10.1007/978-981-15-8443-5_42
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