Stock Prediction using Machine Learning

Dr. J. Dhilipan, D. B. Shanmugam, Imran Quraishi
Long Short Term Memory; LSTM; Tensorflow; Neural Network Module.
Stock trading is one of the foremost activity in finance world. Stock market prediction is used to find the long run values of the stock and other financial factors influenced on a financial exchange. The technical and fundamental or the statistical analysis is employed by most of the stockbrokers while making the stock predictions. Python programming language in machine learning is used for the stock market prediction. In this paper we have proposed a Machine Learning (ML) approach which trains from the available stocks data and gain intelligence and then uses the acquired knowledge for an accurate prediction. In stock market prediction, the aim is to predict the longer term value of the financial stocks of a corporation [1]. The recent trend in market prediction technologies is that the use of machine learning approach which makes predictions supported the values of current stock market indices by training on their previous values. Machine learning itself employs different models to form prediction easier and authentic. This paper focus on Regression and Long Short Term Memory (LSTM) based Machine learning to predict stock values. The factors that are being considered include re-open, close, low, high and volume [2,3].
[1] M. Hagenau, M. Liebmann, and D. Neumann, “Automated news reading: Stock price prediction based on financial news using contextcapturing features,” Decision Support Systems, vol. 55, no. 3, pp. 685– 697, 2013.
[2] X. Zhao, J. Yang, L. Zhao, and Q. Li, “The impact of news on stock market: Quantifying the content of internet-based financial news,” in Proceedings of the 11th International DSI and 16th APDSI Joint meeting, 2011, pp. 12–16.
[3] S. S. Groth and J. Muntermann, “Supporting Investment Management Processes with Machine Learning Technique,” in Business Services: Konzepte, Technologien, Anwendungen – 9, Internationale Tagung Wirtschaftsinformatik, 2009.
[4] G. Fung, J. Yu, and H. Lu, “The predicting power of textual information on financial markets,” IEEE Intelligent Informatics Bulletin, vol. 5, no. 1, 2005.
[5] G. Fung, J. Yu, and W. Lam, “News sensitive stock trend prediction,” in Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, 2002, vol. 2336, pp. 481–493.
[6] R. P. Schumaker and H. Chen, “Textual analysis of stock market prediction using breaking financial news,” ACM Transactions on Information Systems, vol. 27, no. 2, pp. 1–19, 2009.
[7] M. Mittermayer and G. Knolmayer, “Text mining systems for market response to news: A survey,” vol. 41, no. 184. University of Bern, 2006.
[8] S. Deng, T. Mitsubuchi, K. Shioda, T. Shimada, and A. Sakurai, “Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction,” in Proceedings of the IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, 2011, pp. 800–807.
[9] S. Deng, T. Mitsubuchi, K. Shioda, T. Shimada, and A. Sakurai, “Multiple Kernel Learning on Time Series Data and Social Networks for Stock Price Prediction,” in Proceedings of the 10th International Conference on Machine Learning and Applications and Workshops, 2011, pp. 228–234.
[10] X. Li, C. Wang, J. Dong, and F. Wang, “Improving stock market prediction by integrating both market news and stock prices,” Database and Expert Systems Applications, Lecture Notes in Computer Science, vol. 6861, pp. 279–293, 2011.
[11] Chi, L.C., Tang, T.C., and Chin, M. (2012), Corporate transparency as a defense against a stock price plunge: Evidence from a market-crash context. Chiao Da Management Review, 32, 137-162.
[12] Fromlet, H. (2001), Behavioral finance-Theory and practical application, Business Economics, 36(3), 63-69.
[13] Kutan, A.M. and Yuan, S. (2002), Does public information arrival matter in emerging markets?: Evidence from stock exchanges in China, Working paper, Department of Economics and Finance, Southern Illinois University.
[14] Lee, C.W. (1986), Information content of financial column, Journal of Economics and Business, 38, 27-39.
[15] Mitchell, M.L. and Mulheri, J.H. (1994), The impact of public information on the stock market, Journal of Finance, 49(3), 923-950.
[16] Nofsinger, J.R. (2001), The impact of public information in investors, Journal of Banking & Finance, 25(7), 1339-1366.
[17] Tetlock, P.C. (2007), Giving content to investor sentiment: the role of media in the stock market, Journal of Finance, 62(3), 1139-1168.
[18] Tang, T.C. and Chi, L.C. (2014), See what you are searching for?. The proceeding of IAOS 2014 Conference on Official Statistics Meeting the demands of a changing world.
[19] Tetlock, P.C., Maytal, S., and Macskassy, S, (2008), More than words: Quantifying language to measure firms’ fundamentals, Journal of Finance, 63, 1437-1467.
[20] Schumaker, R.P. and Chen, H. (2009), Textual analysis of stock market prediction using breaking financial news: The AZFin text system, ACM Transactions on Information Systems, 27(2), 1-19.
[21] Womack, K.L. (1996), Do brokerage analysts’ recommendations have investment value? Journal of Finance, 51, 137-167.
[22] Womack, K.L. (2012), Do Brokerage Analysts’ Recommendations Have Investment Value? The Journal of Finance, 51(1), 137-167.

Received : 15 March 2021
Accepted : 18 December 2021
Published : 26 December 2021
DOI: 10.30726/ijlca/v8.i4.2021.84008