IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i1d10.1007_s40745-021-00344-x.html
   My bibliography  Save this article

A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting

Author

Listed:
  • Akshit Kurani

    (Indus University)

  • Pavan Doshi

    (Indus University)

  • Aarya Vakharia

    (Indus University)

  • Manan Shah

    (Pandit Deendayal Petroleum University)

Abstract

From exchanging budgetary instruments to tracking individual spending plans to detail a business's profit, money-related organisations utilise computational innovation day by day. Here in this paper, we focus on the significance of innovation in accounts such as financial risk management and stock prediction. We discuss two significant algorithms that have a notable role in stock forecasting. Artificial Neural Networks (ANN), as absenteeism of some data points, does not hamper the network functioning. Secondly, Support Vector Machines (SVM) has several features, and due to simple decision boundaries, it avoids over-fitting. The paper first looks at the different technologies applied in stock market prediction. It examines how sentimental analysis, decision trees, moving average algorithm, and data mining is applied in various stock prediction scenarios. The paper covers the recent past studies to explore the concepts and methodologies through which ANN's and SVM's have been used. Additionally, the paper incorporates significant aspects of novel methods and technologies in which ANN as a hybrid model like ANN-MLP, GARCH-MLP, a combination of the Backpropagation algorithm and Multilayer Feed-forward network, yields better results. Simultaneously, SVM's have been successfully applied in stock prediction, giving an accuracy of about 60%–70% for simple SVM, which is further improved by combining methods like Random Forest, Genetic Algorithm more accurate outcomes. Further, we present our thoughts on where SVM's and ANN's stand as prediction algorithms and challenges like the time constraint, current scenarios, data limitation, and cold start problems were raised. Conclusively SVM and ANN played prominent roles in tackling these issue to an extent and can further be enhanced with their integration with other novel techniques resulting in hybrid methodologies. It will lead students, researchers and financial enthusiasts to more potent approaches for Stock forecasting.

Suggested Citation

  • Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:1:d:10.1007_s40745-021-00344-x
    DOI: 10.1007/s40745-021-00344-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-021-00344-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-021-00344-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Indranil SenGupta & William Nganje & Erik Hanson, 2021. "Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 39-55, March.
    2. Hakob GRIGORYAN, 2016. "A Stock Market Prediction Method Based on Support Vector Machines (SVM) and Independent Component Analysis (ICA)," Database Systems Journal, Academy of Economic Studies - Bucharest, Romania, vol. 7(1), pages 12-21, August.
    3. Ahmet Murat Ozbayoglu & Mehmet Ugur Gudelek & Omer Berat Sezer, 2020. "Deep Learning for Financial Applications : A Survey," Papers 2002.05786, arXiv.org.
    4. XingYu Fu & JinHong Du & YiFeng Guo & MingWen Liu & Tao Dong & XiuWen Duan, 2018. "A Machine Learning Framework for Stock Selection," Papers 1806.01743, arXiv.org, revised Aug 2018.
    5. B. W. Wanjawa & L. Muchemi, 2014. "ANN Model to Predict Stock Prices at Stock Exchange Markets," Papers 1502.06434, arXiv.org.
    6. Aouni, Belaid & Colapinto, Cinzia & La Torre, Davide, 2014. "Financial portfolio management through the goal programming model: Current state-of-the-art," European Journal of Operational Research, Elsevier, vol. 234(2), pages 536-545.
    7. Spronk, Jaap & Hallerbach, Winfried, 1997. "Financial modelling: Where to go? With an illustration for portfolio management," European Journal of Operational Research, Elsevier, vol. 99(1), pages 113-125, May.
    8. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    9. Suellen Teixeira Zavadzki de Pauli & Mariana Kleina & Wagner Hugo Bonat, 2020. "Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction," Annals of Data Science, Springer, vol. 7(4), pages 613-628, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saeed Alqadhi & Hoang Thi Hang & Javed Mallick & Abdullah Faiz Saeed Al Asmari, 2024. "Evaluating landslide susceptibility and landscape changes due to road expansion using optimized machine learning," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(13), pages 11713-11741, October.
    2. Qin, Fuli & Tong, Mingyu & Huang, Ying & Zhang, Yubo, 2024. "Modeling, prediction and analysis of natural gas consumption in China using a novel dynamic nonlinear multivariable grey delay model," Energy, Elsevier, vol. 305(C).
    3. Mirza, Nawazish & Rizvi, Syed Kumail Abbas & Naqvi, Bushra & Umar, Muhammad, 2024. "Inflation prediction in emerging economies: Machine learning and FX reserves integration for enhanced forecasting," International Review of Financial Analysis, Elsevier, vol. 94(C).
    4. Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.
    5. Clemens Tegetmeier & Arne Johannssen & Nataliya Chukhrova, 2024. "Artificial Intelligence Algorithms for Collaborative Book Recommender Systems," Annals of Data Science, Springer, vol. 11(5), pages 1705-1739, October.
    6. Pulikandala Nithish Kumar & Nneka Umeorah & Alex Alochukwu, 2024. "Dynamic graph neural networks for enhanced volatility prediction in financial markets," Papers 2410.16858, arXiv.org.
    7. Thiago Conte & Roberto Oliveira, 2024. "Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazi," Energies, MDPI, vol. 17(4), pages 1-31, February.
    8. Jin, Ting & Liang, Feiyan & Dong, Xiaoqi & Cao, Xiaojuan, 2023. "Research on land resource management integrated with support vector machine —Based on the perspective of green innovation," Resources Policy, Elsevier, vol. 86(PB).
    9. Mokhtar Jlidi & Oscar Barambones & Faiçal Hamidi & Mohamed Aoun, 2024. "ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC," Energies, MDPI, vol. 17(12), pages 1-21, June.
    10. You-Shyang Chen & Jieh-Ren Chang & Ying-Hsun Hung & Jia-Hsien Lai, 2023. "Oversampling Application of Identifying 3D Selective Laser Sintering Yield by Hybrid Mathematical Classification Models," Mathematics, MDPI, vol. 11(14), pages 1-30, July.
    11. Agnieszka Wawrzyniak & Andrzej Przybylak & Piotr Boniecki & Agnieszka Sujak & Maciej Zaborowicz, 2023. "Neural Modelling in the Study of the Relationship between Herd Structure, Amount of Manure and Slurry Produced, and Location of Herds in Poland," Agriculture, MDPI, vol. 13(7), pages 1-13, July.
    12. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    13. Chin Soon Ku & Jiale Xiong & Yen-Lin Chen & Shing Dhee Cheah & Hoong Cheng Soong & Lip Yee Por, 2023. "Improving Stock Market Predictions: An Equity Forecasting Scanner Using Long Short-Term Memory Method with Dynamic Indicators for Malaysia Stock Market," Mathematics, MDPI, vol. 11(11), pages 1-20, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aidin Zehtab-Salmasi & Ali-Reza Feizi-Derakhshi & Narjes Nikzad-Khasmakhi & Meysam Asgari-Chenaghlu & Saeideh Nabipour, 2023. "Multimodal Price Prediction," Annals of Data Science, Springer, vol. 10(3), pages 619-635, June.
    2. Huanyu Ma & Yan Xu & Yulong Liu, 2022. "Prediction of Listed Company Growth in Non-public Economy," Annals of Data Science, Springer, vol. 9(4), pages 847-861, August.
    3. Manoj Verma & Harish Kumar Ghritlahre, 2023. "Forecasting of Wind Speed by Using Three Different Techniques of Prediction Models," Annals of Data Science, Springer, vol. 10(3), pages 679-711, June.
    4. Manoj Verma & Harish Kumar Ghritlahre & Ghrithanchi Chandrakar, 2023. "Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study," Annals of Data Science, Springer, vol. 10(4), pages 851-873, August.
    5. Yue Qi & Ralph E. Steuer & Maximilian Wimmer, 2017. "An analytical derivation of the efficient surface in portfolio selection with three criteria," Annals of Operations Research, Springer, vol. 251(1), pages 161-177, April.
    6. Terence D. Agbeyegbe, 2023. "The Link Between Output Growth and Output Growth Volatility: Barbados," Annals of Data Science, Springer, vol. 10(3), pages 787-804, June.
    7. Deeksha Chandola & Akshit Mehta & Shikha Singh & Vinay Anand Tikkiwal & Himanshu Agrawal, 2023. "Forecasting Directional Movement of Stock Prices using Deep Learning," Annals of Data Science, Springer, vol. 10(5), pages 1361-1378, October.
    8. Panos Xidonas & Ilias Lekkos & Charis Giannakidis & Christos Staikouras, 2023. "Multicriteria security evaluation: does it cost to be traditional?," Annals of Operations Research, Springer, vol. 323(1), pages 301-330, April.
    9. Sandra Caçador & Joana Matos Dias & Pedro Godinho, 2020. "Global minimum variance portfolios under uncertainty: a robust optimization approach," Journal of Global Optimization, Springer, vol. 76(2), pages 267-293, February.
    10. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    11. Ankan Dash & Junyi Ye & Guiling Wang & Huiran Jin, 2024. "High Resolution Solar Image Generation Using Generative Adversarial Networks," Annals of Data Science, Springer, vol. 11(5), pages 1545-1561, October.
    12. Jessie Sun, 2019. "A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models," Papers 1905.04842, arXiv.org.
    13. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    14. Jang Ho Kim & Yongjae Lee & Woo Chang Kim & Frank J. Fabozzi, 2022. "Goal-based investing based on multi-stage robust portfolio optimization," Annals of Operations Research, Springer, vol. 313(2), pages 1141-1158, June.
    15. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    16. Mansoureh Beheshti Nejad & Seyed Mahmoud Zanjirchi & Seyed Mojtaba Hosseini Bamakan & Negar Jalilian, 2024. "Blockchain Adoption in Operations Management: A Systematic Literature Review of 14 Years of Research," Annals of Data Science, Springer, vol. 11(4), pages 1361-1389, August.
    17. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    18. Pätäri, Eero & Karell, Ville & Luukka, Pasi & Yeomans, Julian S, 2018. "Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence," European Journal of Operational Research, Elsevier, vol. 265(2), pages 655-672.
    19. Adebayo Oshingbesan & Eniola Ajiboye & Peruth Kamashazi & Timothy Mbaka, 2022. "Model-Free Reinforcement Learning for Asset Allocation," Papers 2209.10458, arXiv.org.
    20. Amaal Elsayed Mubarak & Ehab Mohamed Almetwally, 2024. "Modelling and Forecasting of Covid-19 Using Periodical ARIMA Models," Annals of Data Science, Springer, vol. 11(4), pages 1483-1502, August.

    More about this item

    Keywords

    Machine learning; ANN; SVM;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:10:y:2023:i:1:d:10.1007_s40745-021-00344-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.