Abstract
Stock market also called as equity market is the aggregation of the sellers and buyers. It is concerned with the domain where the shares of various public listed companies are traded. For predicting the growth of economy, stock market acts as an index. Due to the nonlinear nature, the prediction of the stock market becomes a difficult task. But the application of various machine learning techniques has been becoming a powerful source for the prediction. These techniques employ historical data of the stocks for the training of machine learning algorithms and help in predicting their future behavior. The three machine learning algorithms used in this paper are support vector machine, perceptron, and logistic regression, for predicting the next day trend of the stocks. For the experiment, dataset from about fifty stocks of Indian National Stock Exchange’s NIFTY 50 index was taken, by collecting stock data from January 1, 2013, to December 31, 2018, and lastly by the calculation of some technical indicators. It is reported that the average accuracy for the prediction of the trend of fifty stocks obtained by support vector machine is 87.35%, perceptron is 75.88%, and logistic regression is 86.98%. Since the stock data are time series data, another dataset is prepared by reorganizing previous dataset into the supervised learning format which improves the accuracy of the prediction process which reported the results with support vector machine of 89.93%, perceptron of 76.68%, and logistic regression of 89.93%, respectively.
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References
Balaji AJ, Ram DSH, Nair BB (2018) Applicability of deep learning models for stock price forecasting an empirical study on BANKEX data. Proc Comput Sci 143:947–953
Dash R, Dash PK (2016) A hybrid stock trading framework integrating technical analysis with machine learning techniques. J Finance Data Sci 2:42–57
Deng W, Zhao H, Yang X, Xiong J, Sun M, Li B (2017) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302
Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 7:20281–20292
Fenghua W, Jihong X, Zhifang H, Xu G (2014) Stock price prediction based on SSA and SVM. Proc Comput Sci 31:625–631
Henrique BM, Sobreiro VA, Kimura H (2018) Stock price prediction using support vector regression on daily and up to the minute prices. J Finance Data Sci 4(3):2–36
Lachiheb O, Gouider MS (2018) A hierarchical deep neural network design for stock returns prediction. Proc Comput Sci 126:264–272
Madge S, Bhatt S (2015) Predicting stock price direction using support vector machines. Independent work report. Retrieved from https://pdfs.semanticscholar.org/1f75/856bba0feb216001ba551d249593a9624c01.pdf?_ga=2.178641506.711012539.1587273609-1120604514.1549653500
Moghaddam AH, Moghaddam MH, Esfandyari E (2016) Stock market index prediction using artificial neural network. J Econ Finance Adm Sci 21:89–93
Nahil A, Lyhyaoui A (2018) Short-term stock price forecasting using kernel principal component analysis and support vector machines: the case of Casablanca stock exchange. Proc Comput Sci 127:161–169
Nayak SC, Misra BB, Behera HS (2017) Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices. Ain Shams Eng J 8:371–390
Shi L, Teng Z, Wang L, Zhang Y, Binder A (2018) DeepClue: visual interpretation of text-based deep stock prediction. IEEE Trans Knowl Data Eng Adv Online Publ. https://doi.org/10.1109/tkde.2018.2854193
Wang S, Shang W (2014) Forecasting direction of china security index 300 movement with least squares support vector machine. Proc Comput Sci 3:869–874
Weng B, Ahmed MA, Megahed FM (2017) Stock market one-day ahead movement prediction using disparate data sources. Expert Syst Appl 79:153–163
Zhao H, Liu H, Xu J, Deng W (2019) Performance prediction using high-order differential mathematical morphology gradient spectrum entropy and extreme learning machine. IEEE Trans Instrum Meas. https://doi.org/10.1109/tim.2019.2948414
Zhao H, Zheng J, Deng W, Song Y (2020) Semi-supervised broad learning system based on manifold regularization and broad network. IEEE Trans Circuits Syst. https://doi.org/10.1109/tcsi.2019.2959886
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Parray, I.R., Khurana, S.S., Kumar, M. et al. Time series data analysis of stock price movement using machine learning techniques. Soft Comput 24, 16509–16517 (2020). https://doi.org/10.1007/s00500-020-04957-x
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DOI: https://doi.org/10.1007/s00500-020-04957-x