ML-Based Stock Analysis

Devish*, Deependra Trivedi**, Ankit Khare***
*-*** Department of Information Technology, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India.
Periodicity:June - August'2022
DOI : https://doi.org/10.26634/jit.11.3.18795

Abstract

Researchers are exploring various methods for effectively predicting prices in the stock market. Useful forecasting systems allow traders to better understand data such as future trends. In addition, investors have a great advantage as the analysis provides future market conditions. One such method is the machine learning algorithms for prediction. The aim of this work is to improve the quality of stock market output as predicted using the value of shares.

Keywords

Stock Market, Machine Learning, Technical Analysis, Fundamental Analysis, Sentimental Analysis, Artificial Intelligence, ANN, Support Vector Machine (SVM).

How to Cite this Article?

Devish, Trivedi, D., and Khare, A. (2022). ML-Based Stock Analysis. i-manager’s Journal on Information Technology, 11(3), 20-27. https://doi.org/10.26634/jit.11.3.18795

References

[1]. Ashfaq, R. A. R., Wang, X. Z., Huang, J. Z., Abbas, H., & He, Y. L. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information Sciences, 378, 484-497. https://doi.org/10.1016/j.ins.2016.04.019
[2]. Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932-5941. https://doi.org/10.1016/j.asoc.2015.07.008
[3]. Atsalakis, G. S., & Valavanis, K. P. (2010). Surveying stock market forecasting techniques-Part I: Conventional methods. Journal of Computational Optimization in Economics and Finance, 2(1), 45-92.
[4]. Beck, T., & Levine, R. (2004). Stock markets, banks, and growth: Panel evidence. Journal of Banking & Finance, 28(3), 423-442. https://doi.org/10.1016/S0378-4266(02)00408-9
[5]. Biswal, A., (2022). Stock Price Prediction Using Machine Learning: An Easy Guide. Retrieved from https://www.simplilearn.com/tutorials/machine-learning-tutorial/stock-price-prediction-using-machine-learning.
[6]. Bohn, T. A. (2017). Improving Long Term Stock Market Prediction with Text Analysis. M.S. Thesis, Western University, London, Canada.
[7]. Bouktif, S., Fiaz, A., & Awad, M. (2020). Augmented textual features-based stock market prediction. IEEE Access, 8, 40269-40282. https://doi.org/10.1109/ACCESS.2020.2976725
[8]. Chavan, P. S., & Patil, S. T. (2013). Parameters for stock market prediction. International Journal of Computer Technology and Applications, 4(2), 337.
[9]. Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205. https://doi.org/10.1016/j.eswa.2017.04.030
[10]. Gupta, A., & Dhingra, B. (2012, March). Stock market prediction using hidden markov models. In 2012, Students Conference on Engineering and Systems (pp. 1-4). IEEE. https://doi.org/10.1109/SCES.2012.6199099
[11]. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. MIT press.
[12]. Hu, Y., Liu, K., Zhang, X., Su, L., Ngai, E. W. T., & Liu, M. (2015). Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review. Applied Soft Computing, 36, 534-551.
[13]. Jasic, T., & Wood, D. (2004). The profitability of daily stock market indices trades based on neural network predictions: Case study for the S&P 500, the DAX, the TOPIX and the FTSE in the period 1965–1999. Applied Financial Economics, 14(4), 285-297. https://doi.org/10.1080/0960310042000201228
[14]. Kim, K. J., & Han, I. (2000). Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index. Expert Systems with Applications, 19(2), 125-132. https://doi.org/10.1016/S0957-4174(00)00027-0
[15]. Kim, M. J., Min, S. H., & Han, I. (2006). An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Systems with Applications, 31(2), 241-247. https://doi.org/10.1016/j.eswa.2005.09.020
[16]. Lee, M. C. (2009). Using support vector machine with a hybrid feature selection method to the stock trend prediction. Expert Systems with Applications, 36(8), 10896-10904.
[17]. Liao, Z., & Wang, J. (2010). Forecasting model of global stock index by stochastic time effective neural network. Expert Systems with Applications, 37(1), 834-841. https://doi.org/10.1016/j.eswa.2009.05.086
[18]. Milosevic, N. (2016). Equity forecast: Predicting long term stock price movement using machine learning. arXiv preprint arXiv:1603.00751.
[19]. Schumaker, R. P., & Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 27(2), 1-19.
[20]. Wei, C. C., Chen, T. T., & Lee, S. J. (2013, July). K-NN based neuro-fuzzy system for time series prediction. In 2013, 14th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (pp. 569-574). IEEE.
[21]. Weng, B., Lu, L., Wang, X., Megahed, F. M., & Martinez, W. (2018). Predicting short-term stock prices using ensemble methods and online data sources. Expert Systems with Applications, 112, 258-273. https://doi.org/10.1016/j.eswa.2018.06.016
[22]. Yeh, C. Y., Huang, C. W., & Lee, S. J. (2011). A multiple-kernel support vector regression approach for stock market price forecasting. Expert Systems with Applications, 38(3), 2177-2186. https://doi.org/10.1016/j.eswa.2010.08.004
[23]. Zhang, G., Xu, L., & Xue, Y. (2017). Model and forecast stock market behavior integrating investor sentiment analysis and transaction data. Cluster Computing, 20(1), 789-803.
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