Abstract
Regression analysis is intended to assist investors in identifying practical trends from historical data that aid in the formulation of their investment decisions. Stock market prediction is a technique for estimating future stock and other financial value prices for a corporation. Regression analysis is one of the most effective tools for predicting the stocks and market conditions in the stock market. The volume and velocity of information produced by the stock market are truly staggering. An effort at predicting the direction of the stock market is attempted in this paper. In this work, we have provided a comprehensive review of the most popular efficient regression methods for forecasting daily future stock prices using historical data. For short-term investment, daily prices prediction plays an important role. A mixed kind of linear and non-linear regression (Back-propagation) algorithm is proposed in this work to forecast the opening price of any company's stock. A comparison among linear, non-linear and mixed-type regression models on four widely used stock datasets are discussed and analyzed in terms of RMSE, MAE and prediction accuracy. The proposed mixed-type regression technique provides ~ 99% average prediction accuracy for the opening price.
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References
Enke D, Thawornwong S. The use of data mining and neural networks for forecasting stock market returns. Expert Syst Appl. 2005;29(4):927–40.
Gharehchopogh FS, Mohammadi P, Hakimi P. Application of decision tree algorithm for data mining in healthcare operations: a case study. Int J Comput Appl. 2012;52(6):21–6.
Gharehchopogh FS. Approach and developing data mining method for spatial applications. In: Proceedings of International Conference on Intelligent Systems & Data Processing (ICISD), India. 2011; p. 342–5.
Gharehchopogh FS, Khaze SR. Data mining application for cyber space users tendency in blog writing: a case study. Int J Comput Appl. 2012;47(18):40–6.
Majhi R, Panda G, Sahoo G, Dash PK and Das DP. Stock market prediction of S&P 500 and DJIA using bacterial foraging optimization technique. In: Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. 2007; p. 2569–75. IEEE.
Wuthrich B, Cho V, Leung S, Permunetilleke D, Sankaran K and Zhang J. Daily stock market forecast from textual web data. In: Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference on Vol. 3. 1998; p. 2720–25. IEEE.
Nikfarjam A, Emadzadeh E, & Muthaiyah S. Text mining approaches for stock market prediction. In: Computer and Automation, Engineering (ICCAE), 2010 the 2nd International Conference on Vol. 4. 2010; p. 256–60. IEEE.
Zhang X, Fuehres H, Gloor PA. Predicting stock market indicators through twitter “I hope it is not as bad as I fear.” Procedia Soc Behav Sci. 2011;26:55–62.
Karabulut Y. Can Facebook predict stock market activity?. 2011. http://bus.miami.edu/umbfc/_common/files/papers/Karabulut.pdf [last Available 02.07.2013].
Gharehchopogh FS, Khalifehlou ZA. A New approach in software cost estimation using regression based classifier. AWERProcedia Inf Technol Comput Sci. 2012;2:252–6.
Draper NR, Smith H, Pownell E. Applied regression analysis, vol. 3. New York: Wiley; 1966.
Han J and Kamber M. Data mining concepts and techniques (Vol. 2), ISBN 13: 978-1-55860-901-3.
Senthamarai Kannan K, SailapathiSekar P, Mohamed Sathik M, Arumugam P. Financial stock market forecast using data mining techniques, vol. I. Hongkong: IMECS; 2010.
Desai R, Gandhi S. Stock market prediction using data mining. Int J Eng Dev Res. 2014;2(2):2780–4.
Radaideh QAAL, Abu Asaf A and Alnagi E. Predicting stock prices using data mining techniques. In: International Arab Conference on Information Technology, Sudan, 2013.
Mondal P, Shit L, Goswami S. Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. Int J Comput Sci Eng Appl. 2014;4(2):13–29.
Sarkar S, et al. Prediction using regression analysis. Int Res J Eng Technol. 2016;3(11):829–33.
Sahoo PK, Charlapally K. Stock prediction using regression analysis. Int J Sci Eng Res. 2015;6(3):1655–9.
Dash RK, Nguyen TN, Cengiz K, Sharma A. Fine-tuned support vector regression model for stock predictions. Neural Comput Appl. 2021. https://doi.org/10.1007/s00521-021-05842-w.
Yu M. Linear regression model for stock price of Pfizer. In: Li X, Yuan C, Kent J, editors. Proceedings of the 5th international conference on economic management and green development. Springer, Singapore, 2022; p. 521–5.
Ali SS, Mubeen M, Lal I, Hussain A. Prediction of stock performance by using logistic regression model: evidence from Pakistan Stock Exchange (PSX). Asian J Empirical Res. 2018;8(7):247–58.
Gong J and Sun S. A new approach of stock price prediction based on logistic regression model. In: 2009 International Conference on New Trends in Information and Service Science. 2009; p. 1366–71. IEEE.
Ravikumar S. & Saraf P. Prediction of stock prices using machine learning (regression, classification) Algorithms. In: 2020 International Conference for Emerging Technology (INCET). 2020; p. 1–5. IEEE.
Houssein EH, Dirar M, Abualigah L, Mohamed WM. An efficient equilibrium optimizer with support vector regression for stock market prediction. Neural Comput Appl. 2022;34(4):3165–200.
Khattak A, Khan A, Ullah H, Asghar MU, Arif A, Kundi FM & Asghar MZ. An efficient supervised machine learning technique for forecasting stock market trends. In: Information and Knowledge in Internet of Things. Springer, Cham, 2022;p. 143–62.
Bhoite S, Ansari G, Patil CH, Thatte S, Magar V & Gandhi K. Stock market prediction using recurrent neural network and long short-term memory. In ICT Infrastructure and Computing: Proceedings of ICT4SD 2022. Singapore: Springer Nature Singapore, 2022;p. 635–43.
Bhat SS, Selvam V, Ansari GA, Ansari MD, Rahman MH. Prevalence and early prediction of diabetes using machine learning in North Kashmir: a case study of district bandipora. Comput Intell Neurosci. 2022. https://doi.org/10.1155/2022/2789760.
Rao KV, Ramana Reddy BV. HM-SMF: An Efficient Strategy Optimization using a Hybrid Machine Learning Model for Stock Market Prediction. Int J Image Grap. 2023. https://doi.org/10.1142/S021946782450013X.
Nejad FS & Ebadzadeh MM. Stock market forecasting using DRAGAN and feature matching. arXiv preprint. 2023. arXiv:2301.05693.
Ben Ameur H, Boubaker S, Ftiti Z, Louhichi W & Tissaoui K (2023) Forecasting commodity prices: empirical evidence using deep learning tools. Ann Oper Res 1–19. https://link.springer.com/article/10.1007/s10479-022-05076-6#article-info
Albahli S, Nazir T, Mehmood A, Irtaza A, Alkhalifah A, Albattah W. AEI-DNET: a novel densenet model with an autoencoder for the stock market predictions using stock technical indicators. Electronics. 2022;11(4):611.
Park HJ, Kim Y, Kim HY. Stock market forecasting using a multi-task approach integrating long short-term memory and the random forest framework. Appl Soft Comput. 2022;114: 108106.
https://in.investing.com/equities/hindustan-construction-company-historical-data.
https://in.investing.com/equities/coal-india-historical-data.
https://www.kaggle.com/datasets/altruisticemphasis/bharti-airtel-stock
Wang S. Nonlinear regression: a hybrid model. Comput Oper Res. 1999;26(8):799–817.
Chandar SK, Sumathi M, Sivanandam SN. Prediction of stock market price using hybrid of wavelet transform and artificial neural network. Indian J Sci Technol. 2016;9(8):1–5.
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Chakraborty, S., Kairi, A., Dutta Roy, N. et al. Market-Based Stock Allocation Using a Hybrid Regression Model. SN COMPUT. SCI. 4, 423 (2023). https://doi.org/10.1007/s42979-023-01883-1
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DOI: https://doi.org/10.1007/s42979-023-01883-1