stock market prediction research paper pdf

• DOI: 10.54254/2754-1169/87/20240899
• Corpus ID: 270354278

Prediction of Stock Price by Neural Network Based on CNN, LSTM, ANN

• Hanqing Wen
• Published in Advances in Economics… 7 June 2024
• Computer Science, Business

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A Novel Approach for Forecasting Price of Stock Market using Machine Learning Techniques

• Original Research
• Published: 28 June 2024
• Volume 5 , article number  686 , ( 2024 )

Cite this article

• Abhinay Yadav 1 ,
• Vineet Kumar 1 ,
• Satyendra Singh 1 &
• Ashish Kumar Mishra   ORCID: orcid.org/0000-0002-7532-5585 1  

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In today’s competitive business world, industries strive for rapid growth and leadership. Expanding a business requires additional capital, which can be raised through an initial public offering (IPO), angel investors, or business loans. As a company grows, it becomes difficult for individual investors to sustain operations with their capital alone, necessitating a constant influx of funds. Conducting an IPO not only raises capital but also enhances the company’s reputation and credibility. It can also allow founders or early-stage investors to sell part of their ownership. After an IPO, the company’s shares are publicly traded as stocks, offering various benefits when included in a public investment portfolio. Investing in stocks from different companies enables individuals to accumulate savings and safeguard their wealth against inflation and taxes. However, accurately predicting stock prices is crucial for maximizing investment returns. In this research paper, the main goal is to predict the stock price. To achieve this, a special Hybrid model called LSTM + GRU is used. Two case studies have also been done to support the result. The first case study is done with Tata Motors and the other is with Honda Motors. Different measures, such as RMSE, MAE, and MSE, are used to assess how well the model performs. The results are presented in a visually appealing way, allowing for easy understanding and comparison with other existing models. By conducting this research, our objective is to provide valuable insights into predicting stock prices, helping investors and decision-makers make informed choices

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Data availability.

The datasets generated and analyzed during the research are available from the corresponding authors upon reasonable request.

Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J. LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst. 2016;28(10):2222–32.

Article   MathSciNet   Google Scholar  

Reddy VKS. Stock market prediction using machine learning. Int Res J Eng Technol (IRJET). 2018;5(10):1033–5.

Google Scholar  

Wang H. Stock price prediction based on machine learning approaches. In: Proceedings of the 3rd international conference on data science and information technology. 2020. p. 1–5.

Adhikar AJ, Jadhav AK, KH CG, HS MS. Literature survey on stock price prediction using machine learning. Int J Eng Appl Sci Technol. 2020;5(8):2143–455.

Kadam MY, Kulkarni MS, Lonsane, MS, Khandagale AS. A survey on stock market price prediction system using machine learning techniques. 2022.

Torres PEP, Hernández-Álvarez M, Torres Hernández EA, Yoo SG. Stock market data prediction using machine learning techniques. In: Information technology and systems: proceedings of ICITS 2019. Springer International Publishing; 2019. p. 539–47.

Nikou M, Mansourfar G, Bagherzadeh J. Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intell Syst Account Finance Manag. 2019;26(4):164–74.

Article   Google Scholar  

Rezaei H, Faaljou H, Mansourfar G. Stock price prediction using deep learning and frequency decomposition. Expert Syst Appl. 2021;169: 114332.

Karim ME, Foysal M, Das S. Stock price prediction using Bi-LSTM and GRU-based hybrid deep learning approach. In: Proceedings of third doctoral symposium on computational intelligence: DoSCI 2022. Singapore: Springer Nature Singapore; 2022. p. 701–11.

Thakkar A, Chaudhari K. A comprehensive survey on deep neural networks for stock market: the need, challenges, and future directions. Expert Syst Appl. 2021;177: 114800.

Hossain MA, Karim R, Thulasiram R, Bruce ND, Wang Y. Hybrid deep learning model for stock price prediction. In: 2018 IEEE symposium series on computational intelligence (ssci). IEEE; 2018. p. 1837–44.

Babu CN, Reddy BE. Selected Indian stock predictions using a hybrid ARIMA-GARCH model. In: 2014 international conference on advances in electronics computers and communications. IEEE; 2014. p. 1–6.

Vanipriya CH, Thammi Reddy K. Indian stock market predictor system. In: ICT and critical infrastructure: proceedings of the 48th annual convention of Computer Society of India-Vol II: hosted by CSI Vishakapatnam Chapter. Springer International Publishing; 2014. p. 17–26.

Bukhari AH, Raja MAZ, Sulaiman M, Islam S, Shoaib M, Kumam P. Fractional neuro-sequential ARFIMA-LSTM for financial market forecasting. IEEE Access. 2020;8:71326–38.

Gao Y, Wang R, Zhou E. Stock prediction based on optimized LSTM and GRU models. Sci Progr. 2021;2021:1–8.

Koukaras P, Nousi C, Tjortjis C. Stock market prediction using microblogging sentiment analysis and machine learning. In: Telecom, vol. 3, no. 2. MDPI; 2022. p. 358–78.

Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: a review of classification techniques. Emerg Artif Intell Appl Comput Eng. 2007;160(1):3–24.

Sadia KH, Sharma A, Paul A, Padhi S, Sanyal S. Stock market prediction using machine learning algorithms. Int J Eng Adv Technol. 2019;8(4):25–31.

Jakub A. Make kNN 300 times faster than Scikit-learn’s in 20 lines! towardsdatascience.com. 2020. https://towardsdatascience.com/make-knn-300-times-faster-than-scikit-learns-in-20-lines-5e29d74e76bb . Accessed 30 Oct 2022.

Huynh HD, Dang LM, Duong D. A new model for stock price movements prediction using deep neural network. In: Proceedings of the 8th international symposium on information and communication technology. 2017. p. 57–62.

Kukreti V, Bhatt C, Dani R. A stock market trends analysis of reliance using machine learning techniques. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON). IEEE; 2023. p. 1–6.

Avramov D, Chordia T, Jostova G, Philipov A. Bonds, stocks, and sources of mispricing. George Mason University School of Business Research paper. 2019. p. 18–5.

Qiu J, Wang B, Zhou C. Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLoS One. 2020;15(1): e0227222.

Nelson DM, Pereira AC, De Oliveira RA. Stock market’s price movement prediction with LSTM neural networks. In: 2017 International joint conference on neural networks (IJCNN). IEEE; 2017. p. 1419–26.

Budhani N, Jha CK, Budhani SK. Prediction of stock market using artificial neural network. In: 2014 international conference of soft computing techniques for engineering and technology (ICSCTET). IEEE; 2014. p. 1–8.

Recurrent neural networks. Research Gate. 2019. Accessed 30 Oct 2022.

Rouf N, Malik MB, Arif T, Sharma S, Singh S, Aich S, Kim HC. Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics. 2021;10(21):2717.

Umer M, Awais M, Muzammul M. Stock market prediction using machine learning (ML) algorithms. ADCAIJ Adv Distrib Comput Artif Intell J. 2019;8(4):97–116.

Selvin S, Vinayakumar R, Gopalakrishnan EA, Menon VK, Soman KP. Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 international conference on advances in computing, communications and informatics (icacci). IEEE; 2017. p. 1643–47.

Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. arXiv:1412.3555 .

Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures. In: International conference on machine learning. PMLR; 2015. p. 2342–50.

Akita R, Yoshihara A, Matsubara T, Uehara K. Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE/ACIS 15th international conference on computer and information science (ICIS). IEEE; 2016. p. 1–6.

Minh DL, Sadeghi-Niaraki A, Huy HD, Min K, Moon H. Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access. 2018;6:55392–404.

Althelaya KA, El-Alfy ESM, Mohammed S. Stock market forecast using multivariate analysis with bidirectional and stacked (LSTM, GRU). In: 2018 21st Saudi computer society national computer conference (NCC). IEEE; 2018. p. 1–7.

Khan U, Aadil F, Ghazanfar MA, Khan S, Metawa N, Muhammad K, Nam Y. A robust regression-based stock exchange forecasting and determination of correlation between stock markets. Sustainability. 2018;10(10):3702.

thingSpeakRead.Mathworks. (n.d.). https://www.mathworks.com/help/thingspeak/calculate-simple-moving-average.html . Accessed 30 Oct 2022.

Biau G, Devroye L. Lectures on the nearest neighbor method, vol. 246. Cham: Springer International Publishing; 2015.

Book   Google Scholar  

Pagolu VS, Reddy KN, Panda G, Majhi B. Sentiment analysis of Twitter data for predicting stock market movements. In: 2016 international conference on signal processing, communication, power and embedded system (SCOPES). IEEE; 2016. p. 1345–50.

Khare K, Darekar O, Gupta P, Attar VZ. Short term stock price prediction using deep learning. In: 2017 2nd IEEE international conference on recent trends in electronics, information & communication technology (RTEICT). IEEE; 2017. p. 482–86.

Shewalkar A, Nyavanandi D, Ludwig SA. Performance evaluation of deep neural networks applied to speech recognition: RNN, LSTM and GRU. J Artif Intell Soft Comput Res. 2019;9(4):235–45.

Hu Z, Zhao Y, Khushi M. A survey of forex and stock price prediction using deep learning. Appl Syst Innov. 2021;4(1):9.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.

Sarode S, Tolani H G, Kak P, Lifna CS. Stock price prediction using machine learning techniques. In: 2019 international conference on intelligent sustainable systems (ICISS). IEEE; 2019. p. 177–81.

XIAOQIANG. What is a support vector machine? easyai.tech. 2019. https://easyai.tech/en/ai-definition/svm . Accessed 30 Oct 2022.

Gururaj V, Shriya VR, Ashwini K. Stock market prediction using linear regression and support vector machines. Int J Appl Eng Res. 2019;14(8):1931–4.

Kostadinov S. Gated Recurrent Unit. Understanding GRU Networks. 2017. https://medium.com/towards-data-science/understanding-gru-networks-2ef37df6c9be . Accessed 30 Oct 2022.

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Department of Information Technology, Rajkiya Engineering College Ambedkar Nagar, Akbarpur Ambedkar Nagar, U.P., 224122, India

Abhinay Yadav, Vineet Kumar, Satyendra Singh & Ashish Kumar Mishra

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Yadav, A., Kumar, V., Singh, S. et al. A Novel Approach for Forecasting Price of Stock Market using Machine Learning Techniques. SN COMPUT. SCI. 5 , 686 (2024). https://doi.org/10.1007/s42979-024-02916-z

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Received : 29 September 2023

Accepted : 19 April 2024

Published : 28 June 2024

DOI : https://doi.org/10.1007/s42979-024-02916-z

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Prediction of Stock Market Using Artificial Intelligence

Proceedings of the 4th International Conference on Advances in Science & Technology (ICAST2021)

6 Pages Posted: 22 Jun 2021

Akash Patel

K J Somaiya Institute of Engineering and Information Technology, Mumbai,

Devang Patel

Seema yadav.

Date Written: May 7, 2021

Abstract—Stock market is place where people buy and sell shares of publicly listed companies. Every buyer and seller try to predict the stock market price movements to get maximum profits and minimum losses. Using cutting edge technology such as AI can improve prediction stock price. In the procedure of considering strategies and variables to be considered, we found ML algorithmics such as Random forest, LSTM, SVM, ANN was not fully utilized. In this model we will introduce and review more a possible way to predict stock movements with high accuracy. The first thing we considered is data of previous year's share market prices, historical prices of currency and commodity market and the historical news headlines. The datasets were pre-processed and prepared for actual analysis. Therefore, our model will also focus on preprocessing of datasets. Second, after processing the datasets earlier, we will review the use of major AI technique for that data and productive results. In addition, the proposed system evaluates the application of the forecast system to the real-world scenario and the problems associated with the accuracy of the total values provided. The high accuracy and profitability was achieved when results of all algorithms are combined and considered all factors affecting the stock prices. Successful valuation prediction of share price can become a big asset for stock market firms and provide real life solutions to the difficulties faced by stock market individual investors have.

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Akash Patel (Contact Author)

K j somaiya institute of engineering and information technology, mumbai, ( email ).

Somaiya Ayurvihar Complex Eastern Express Highway Mumbai, MA Maharashtra 400022 India

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27 papers with code • 1 benchmarks • 2 datasets

Stock Price Prediction is the task of forecasting future stock prices based on historical data and various market indicators. It involves using statistical models and machine learning algorithms to analyze financial data and make predictions about the future performance of a stock. The goal of stock price prediction is to help investors make informed investment decisions by providing a forecast of future stock prices.

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