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ISSN (Online): 2666-2566

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Research Article

Comparative Study of Machine Learning Algorithms towards Predictive Analytics

Author(s): Maheswari Petchiappan* and Jaya Aravindhen

Volume 16, Issue 6, 2023

Published on: 17 October, 2022

Article ID: e230622206361 Pages: 11

DOI: 10.2174/2666255816666220623160821

Price: $65

Abstract

Background: The trend of the stock market prediction has always been challenging and confusing for investors. There is tremendous growth in stock market prediction with the advancement of technology, machine learning, data science, and big data. The media and entertainment sector is one of the diverse sectors in the stock market. In the Indian stock market, Sensex and Nifty are the two indexes. The 2019 pandemic forced the movie theatres to shut down. As a result, distributors and film directors were not able to release their movies in theatres, and production was also stopped. Consequently, during the lockdown, people spent more time at home watching electronic media, resulting in a higher degree of media consumption.

Objectives: The objective of the research is to predict the performance of the media and entertainment companies stock prices using machine-learning techniques. Investors will be benefited by maximizing the profit and minimizing the loss.

Methods: The proposed stock prediction system is used to predict the stock values and find the accuracy of linear regression and logistic regression in machine learning algorithms for data science.

Results: The experiments are conducted for the media and entertainment stock price data using Machine-learning algorithms. Media stock prices are considered as the input dataset. The model has been developed using the daily frequency of stock prices with different attributes.

Conclusion: Thus, the media and entertainment stocks are predicted using linear regression and logistic regression. Using the above techniques, stock prices are predicted accurately to maximize profits and minimize the loss for the investors.

Keywords: Stock market, prediction, machine learning, linear regression, logistic regression, supervised learning, and data science.

Graphical Abstract

[1]
J. Shen, and M.O. Shafiq, "Short-term stock market price trend prediction using a comprehensive deep learning system", J. Big Data, vol. 7, no. 1, p. 66, 2020.
[http://dx.doi.org/10.1186/s40537-020-00333-6] [PMID: 32923309]
[2]
H.L. Siew, and M.J. Nordin, "Regression techniques for the prediction of stock price trend", In 2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE), 10-12 Sept, 2012, Langkawi, Malaysia, 2012, pp. 1-5
[http://dx.doi.org/10.1109/ICSSBE.2012.6396535]
[3]
S. Sajid, A. Haleem, S. Bahl, M. Javaid, T. Goyal, and M. Mittal, "Data science applications for predictive maintenance and materials science in context to Industry 4.0", Mater. Today Proc., vol. 45, pp. 4898-4905, 2021.
[http://dx.doi.org/10.1016/j.matpr.2021.01.357]
[4]
S. Virkus, and E. Garoufallou, "Data science from a library and information science perspective", Data Technol. Appl., vol. 53, no. 4, pp. 422-441, 2019.
[http://dx.doi.org/10.1108/DTA-05-2019-0076]
[5]
W. Qi, "“Application of BP artificial neural network in stock forecasting”, J. Harbin Univ. Commer", Nat. Sci. Educ., no. 2, pp. 237-240, 2019.
[6]
F. Wei, X. Li, H. Cao, and P. Peng, "Stock trend prediction based on improved random forest algorithm", J. Hangzhou Dianzi University, vol. 39, no. 02, pp. 22-27, 2019.
[7]
I.R. Parray, S.S. Khurana, M. Kumar, and A.A. Altalbe, "Timeseries data analysis of stock price movement using machine learning techniques", Soft Comput., vol. 24, no. 21, pp. 16509-16517, .
[8]
M.A. Ghazanfar, S.A. Alahmari, Y.F. Aldhafiri, A. Mustaqeem, M. Maqsood, and M.A. Azam, "Using machine learning classifiers to predict stock exchange index", Int. J. Mach. Learn. Comput., vol. 7, no. 2, pp. 24-29, 2017.
[http://dx.doi.org/10.18178/ijmlc.2017.7.2.614]
[9]
J. Gong, and S. Sun, "A new approach of stock price trend prediction based on logistic regression model", In 2009 International Conference on New Trends in Information and Service Science, 30 Jun- 2 July 2009, Beijing, China, 2009, pp. 1366-1371
[http://dx.doi.org/10.1109/NISS.2009.267]
[10]
G. Uma, and S. Kotrappa, "Impact of covid 19 on stock market performance using efficient and predictive LBL-LSTM based mathematical model", Int. J. Emerg. Technol., vol. 11, no. 4, pp. 108-115, 2020.
[11]
M. Smita, "Logistic regression model-A review", Int. J. of Innov. Sci. Res. Technol., vol. 6, no. 5, pp. 2456-2165, 2021.
[12]
G.O.H. Thomas Sumarsan, and H. Albert, "Determinants and prediction of the stock market during COVID-19: Evidence from Indonesia", J. Asian Fin. Econ. Bus., vol. 8, no. 1, pp. 1-6, 2021.
[13]
K. Pahwa, and N. Agarwal, "Stock market analysis using supervised machine learning", International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Com-IT-Con), 14th -16th Feb, 2019, Faridabad, India, 2019, pp. 197-200.
[14]
D. Bhuriya, G. Kaushal, A. Sharma, and U. Singh, "Stock market prediction using a linear regression", In 2017 International Conference on Electronics, Communication and Aerospace Technology (ICECA), 20-22 Apr, 2017, vol. 2, Coimbatore, Indiapp. 510-513
[http://dx.doi.org/10.1109/ICECA.2017.8212716]
[15]
F. Zhou, and Q. Zhang, "Cascading logistic regression onto gradient boosted decision trees for forecasting and trading stock indices", Appl. Soft Comput. J., vol. 84, p. 105747, 2019.
[http://dx.doi.org/10.1016/j.asoc.2019.105747]
[16]
I. Kumar, K. Dogra, C. Utreja, and P. Yadav, "A comparative study of supervised machine learning algorithms for stock market trend prediction", In 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 20-21 Apr, 2018, Coimbatore, India, 2018, pp. 1003-1007
[http://dx.doi.org/10.1109/ICICCT.2018.8473214]
[17]
I. Parmar, "Stock market prediction using machine learning", In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 15-17 Dec, 2018, Jalandhar, India, 2018, pp. 574-576
[http://dx.doi.org/10.1109/ICSCCC.2018.8703332]
[18]
M. Usmani, S. Adil, K. Raza, and S.S. Ali, "Stock market prediction using machine learning techniques", In 2016 3rd International Conference on Computer and Information Sciences (ICCOINS), 15-17 Aug, 2016, Kuala Lumpur, Malaysia, 2016, pp. 322-327
[http://dx.doi.org/10.1109/ICCOINS.2016.7783235]
[19]
K.V. Sujatha, and S.M. Sundaram, "Stock index prediction using regression and neural network models under non-normal conditions", Interact, pp. 59-63, 2010.
[http://dx.doi.org/10.1109/INTERACT.2010.5706195]
[20]
M. Billah, S. Waheed, and A. Hanifa, "Stock market prediction using an improved training algorithm of the neural network", In 2016 2nd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), 8-10 Dec, 2016, Rajshahi, Bangladesh, 2016, pp. 1-4
[http://dx.doi.org/10.1109/ICECTE.2016.7879611]
[21]
P. Sajda, "Machine learning for detection and diagnosis of disease", Annu. Rev. Biomed. Eng., vol. 8, no. 1, pp. 537-565, 2006.
[22]
S. Nusinovici, Y.C. Tham, Y.C.Y. Marco, S.W.T. Daniel, J. Li, C. Sabanayagam, T.Y. Wong, and C.Y. Cheng, "Logistic regression was as good as machine learning for predicting major chronic diseases", J. Clin. Epidemiol., vol. 122, pp. 56-69, 2020.
[http://dx.doi.org/10.1016/j.jclinepi.2020.03.002]
[23]
M. Awan, M. Rahim, H. Nobanee, A. Munawar, A. Yasin, A. Zain, and M. Javed, "Social media and stock market prediction: A big data approach", Comput. Mater. Continua, vol. 67, no. 2, pp. 2569-2583, 2021.
[http://dx.doi.org/10.32604/cmc.2021.014253]
[24]
C. Hargreaves, and Y. Hao, "Prediction of stock performance using analytical techniques", J. Emerg. Technol. Web Intell., vol. 5, no. 2, pp. 136-142, 2013.
[http://dx.doi.org/10.4304/jetwi.5.2.136-142]
[25]
S. Mokhtari, "Effectiveness of artificial intelligence in stock market prediction based on machine learning", arXiv preprint arXiv:2107.01031, 2021.
[26]
I.H. Sarker, "Machine learning: Algorithms, real-world applications and research directions", Comput. Sci., vol. 2, no. 3, p. 160, 2021.
[http://dx.doi.org/10.1007/s42979-021-00592-x] [PMID: 33778771]
[27]
I.K. Nti, A.F. Adekoya, and B.A. Weyori, "A systematic review of fundamental and technical analysis of stock market predictions", Artif. Intell. Rev., vol. 53, no. 4, pp. 3007-3057, 2020.
[http://dx.doi.org/10.1007/s10462-019-09754-z]
[28]
P. Paik, and B. Kumari, "Stock market prediction using ANN, SVM, ELM: A review", IJETTCS, vol. 6, no. 3, pp. 88-94, 2017.
[http://dx.doi.org/10.1038/33071]
[29]
A. Archit, "A survey of machine learning techniques used on Indian stock market", IOP Conf. Ser.: Mater. Sci. Eng., vol. 1042, p. 012021, 2021.
[30]
R.K. Nayak, D. Mishra, and A.K. Rath, "A Naïve SVM-KNN based stock market trend reversal analysis for Indian benchmark indices", Appl. Soft Comput., vol. 35, pp. 670-680, 2015.
[http://dx.doi.org/10.1016/j.asoc.2015.06.040]
[31]
S.R. Nanda, B. Mahanty, and M.K. Tiwari, "Clustering Indian stock market data for portfolio management", Expert Syst. Appl., vol. 37, no. 12, pp. 8793-8798, 2010.
[http://dx.doi.org/10.1016/j.eswa.2010.06.026]
[32]
B.B. Nair, and V.P. Mohandas, "An intelligent recommender system for stock trading", Intell. Decision Technol., vol. 9, no. 3, pp. 243-269, 2015.
[http://dx.doi.org/10.3233/IDT-140220]
[33]
D. Oyewola, A. Ibrahim, J. Kwanamu, and E. Dada, "A new auditory algorithm in stock market prediction on oil and gas sector in Nigerian stock exchange", Soft Comput. Lett., vol. 3, p. 100013, 2021.
[http://dx.doi.org/10.1016/j.socl.2021.100013]
[34]
W. Lu, J. Li, Y. Li, A. Sun, and J. Wang, "A CNN-LSTM-based model to forecast stock prices", Complexity, vol. 2020, pp. 1-10, 2020.
[http://dx.doi.org/10.1155/2020/6622927]
[35]
A. Nakayama, "Short-term stock price prediction by analysis of order pattern images", 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2019, pp. 1-5.
[http://dx.doi.org/10.1109/CIFEr.2019.8759057]
[36]
V. Palekar, P. Zade, B. Vaidya, R. Mehra, N. Pradhan, and A. Lambat, "Company’s stock price predictor using machine learning", Int. Res. J. Eng. Tecnol., vol. 7, no. 3, p. 2395, 2020.
[37]
T. Paul, "Fintech empowers prediction of stock market index using artificial neural network", In 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), 29-30 June, 2021, Yogyakarta, Indonesia, 2021, pp. 42-46
[http://dx.doi.org/10.1109/ICAICST53116.2021.9497817]
[38]
F. Mari, U. Pratiwi, I. Oktanisa, and F. Utaminingrum, "Comparative study of numerical methods in multiple linear regression for stock prediction Jakarta Islamic Index (JII", In 2019 International Conference on Sustainable Information Engineering and Technology (SIET), 28-30 Sept, 2019, Lombok, Indonesia, 2019, pp. 110-115
[http://dx.doi.org/10.1109/SIET48054.2019.8985999]
[39]
R. Couronné, P. Probst, and A.L. Boulesteix, "Random forest versus logistic regression: A large-scale benchmark experiment", BMC Bioinformatics, vol. 19, no. 1, p. 270, 2018.
[http://dx.doi.org/10.1186/s12859-018-2264-5] [PMID: 30016950]
[40]
J. Amrutphale, P. Rathore, and V. Malviya, "A novel approach for stock market price prediction based on polynomial linear regression", In: R. Shukla, J. Agrawal, S. Sharma, N. Chaudhari, K. Shukla, Eds., Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems., vol. 100. Springer: Singapore, 2020.
[http://dx.doi.org/10.1007/978-981-15-2071-6_13]
[41]
L.P. Maguluri, and R. Ragupathy, "A cluster-based non-linear regression framework for periodic multi-stock trend prediction on real-time stock market data", Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 9, pp. 537-551, 2020.
[42]
F.S. Gharehchopogh, T.H. Bonab, and S.R. Khaze, "A linear regression approach to prediction of stock market trading volume: A case study", Int. J. Manag. Value Supply Chains, vol. 4, no. 3, pp. 25-31, 2013.
[http://dx.doi.org/10.5121/ijmvsc.2013.4303]
[43]
M. Gurjar, P. Naik, G. Mujumdar, and T. Vaidya, "Stock market prediction using ANN", Int. Res. J. Eng. Technol., vol. 5, no. 3, pp. 2758-2761, 2018.
[44]
M. Hiransha, E.A. Gopalakrishnan, V.K. Menon, and K.P. Soman, "NSE stock market prediction using deep-learning models", Procedia Comput. Sci., vol. 132, pp. 1351-1362, 2018.
[http://dx.doi.org/10.1016/j.procs.2018.05.050]
[45]
M. Nikou, G. Mansourfar, and J. Bagherzadeh, "Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms", Intell. Syst. Acc. Fin. Manag., vol. 26, no. 4, pp. 164-174, 2019.
[http://dx.doi.org/10.1002/isaf.1459]
[46]
A. Parot, K. Michell, and W.D. Kristjanpoller, "Using artificial neural networks to forecast exchange rate, including VAR–VECM residual analysis and prediction linear combination", Int. J. Intell. Syst. Account. Finance Manage., vol. 26, no. 1, pp. 3-15, 2019.
[http://dx.doi.org/10.1002/isaf.1440]
[47]
E.K. Ampomah, Z. Qin, and G. Nyame, "Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement", Information, vol. 11, no. 6, p. 332, 2020.
[http://dx.doi.org/10.3390/info11060332]
[48]
D. Selvamuthu, V. Kumar, and A. Mishra, "Indian stock market prediction using artificial neural networks on tick data", Finance Innov, vol. 5, no. 1, p. 16, 2019.
[http://dx.doi.org/10.1186/s40854-019-0131-7]
[49]
J. Verma, S. Tanwar, S. Garg, I. Gandhi, and N. Bachani, "Evaluation of pattern based customized approach for stock market trend prediction with big data and machine learning techniques", Int. J. Bus. Anal., vol. 6, no. 3, pp. 1-15, 2019.
[http://dx.doi.org/10.4018/IJBAN.2019070101]
[50]
Q. Zheng, and M. Yang, "A full stage data augmentation method in deep convolutional neural network for natural image classification", Discrete Dyn. Nat. Soc., vol. 2020, p. 4706576, 2020.
[http://dx.doi.org/10.1155/2020/4706576]
[51]
Z. Qinghe, M. Yang, J. Yang, Q. Zhang, and X. Zhang, "Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process", In: IEEE Access, vol. 6, pp. 15844-15869, 2018.
[http://dx.doi.org/10.1109/ACCESS.2018.2810849]
[52]
M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning., 2nd ed The MIT Press: London, England, 2018.http://library1.nida.ac.th/termpaper6/sd/2554/19755.pdf
[53]
S.J. Russell, and P. Norvig, Artificial Intelligence: A Modern Approach., Prentice-Hall: Englewood Cliffs, New Jersey, 1995.
[54]
S.J. Russell, J. Stuart, P. Norvig, and J. Canny, Artificial Intelligence: A Modern Approach., Pearson Education, 2003, p. 1081.
[55]
X.D. Zhang, Machine learning. A Matrix Algebra Approach to Artificial Intelligence., Springer: Singapore, 2020.
[http://dx.doi.org/10.1007/978-981-15-2770-8_6]
[56]
J.F. Hair, Multivariate Data Analysis with Readings., 4th ed Prentice-Hall: Englewood Cliffs, NJ, 1995.
[57]
M. Talabis, R. McPherson, I. Miyamoto, and J. Martin, "Analytics Defined," in Information Security Analytics., Oxford: Syngress, 2014, pp. 1-12.
[58]
Sun TV Network, Available from: https://en.wikipedia.org/wiki/Sun_TV_Network
[59]
Zee, Available from: https://en.wikipedia.org/wiki/Zee
[60]
Network18 Group., Available from: https://en.wikipedia.org/wiki/Network18_Group
[61]
INOX Leisure., Available from: https://en.wikipedia.org/wiki/INOX_Leisure_Limited (Accessed on June 10, 2022).
[62]
PVR Cinemas., Available from: https://en.wikipedia.org/wiki/PVR_Cinemas (Accessed on June 10, 2022).
[63]
Available from: https://en.wikipedia.org/wiki/ (Accessed on June 10, 2022).
[64]
DB Corp., Available from: https://en.wikipedia.org/wiki/DB_Corp (Accessed on June 10, 2022).
[65]
Dish TV., Available from: https://en.wikipedia.org/wiki/Dish_TV (Accessed on June 10, 2022).
[66]
Hathway Cable & Datacom Ltd., Available from: https://en.wikipedia.org/wiki/Hathway (Accessed on June 10, 2022).
[67]
Delta Corp, Available from: https://en.wikipedia.org/wiki/Delta_Corp (Accessed on June 10, 2022)
[68]
Hiregoudar S., Ways to evaluate regression models.. Available from: https://medium.com/towards-data-science/ways-to-evaluate-regression-models-77a3ff45ba70 Available from: https://towardsdatascience.com/ways-to-evaluate- (Accessed on June 10, 2022).
[69]
Caya P.O., Implementing Binary Logistic Regression in R.. Available from: https://medium.com/pete-caya/implementing-binary-logistic-regression-in-r-e3a6f59ae294 Available from: https://towardsdatascience.com/implementing-binary-logistic (Accessed on June 10, 2022).
[70]
Yahoo! India Finance, Available from: https://in.finance.yahoo.com (Accessed on June 10, 2022).

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