IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v14y2023i1d10.1007_s13198-022-01801-3.html
   My bibliography  Save this article

Prediction of stock price growth for novel greedy heuristic optimized multi-instances quantitative (NGHOMQ)

Author

Listed:
  • Subba Rao Polamuri

    (Kakinada Institute of Engineering and Technology)

  • K. Srinnivas

    (V. R. Siddhartha Engineering College)

  • A. Krishna Mohan

    (Univerity College of Engineering Kakinada, JNTUK)

Abstract

A large volume of data is constantly accumulating in contemporary information technology relating to sophisticated computing. Stock price prediction is a popular topic and a difficult task due to the randomness and complexity of stock market-related computing systems. As stock market information web content develops, researchers and investors typically extract different indicator aspects, such as attitudes and events, from stock market real-time financial data predictions. Because of the current financial and unknown aspects in the stock market arena, stock price prediction is a difficult task, even though conventional authors worked on neural networks to improve stock price prediction in several financial domains. In this research, I present a Novel Greedy Heuristic Optimized Multi-instance Quantitative (NGHOMQ) strategy to investigate needed data from factors while rejecting their parameter interactions to improve index-based composite stock market movement prediction in multi-instance quantitative data. By employing unique heuristic calculations to describe sequential stock price-related events, it may be used to blend attitudes and events as well as to analyze quantitative data in a comprehensive way. In heuristic modes with numerous instances, Pareto optimization is utilized to prevent stock price prediction based on optimal statistical performance. Furthermore, our proposed approach may detect data input to produce stock market price forecasts in financial computing technologies. The efficiency of NGHOMQ compared to traditional neural network related frameworks and approaches is demonstrated using data from the Indian stock market.

Suggested Citation

  • Subba Rao Polamuri & K. Srinnivas & A. Krishna Mohan, 2023. "Prediction of stock price growth for novel greedy heuristic optimized multi-instances quantitative (NGHOMQ)," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 353-366, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01801-3
    DOI: 10.1007/s13198-022-01801-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-022-01801-3
    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/s13198-022-01801-3?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. Jiayu Qiu & Bin Wang & Changjun Zhou, 2020. "Forecasting stock prices with long-short term memory neural network based on attention mechanism," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-15, January.
    2. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    Full references (including those not matched with items on IDEAS)

    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. Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    2. Fernandes, Leonardo H.S. & Bouri, Elie & Silva, José W.L. & Bejan, Lucian & de Araujo, Fernando H.A., 2022. "The resilience of cryptocurrency market efficiency to COVID-19 shock," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    3. Diniz-Maganini, Natalia & Diniz, Eduardo H. & Rasheed, Abdul A., 2021. "Bitcoin’s price efficiency and safe haven properties during the COVID-19 pandemic: A comparison," Research in International Business and Finance, Elsevier, vol. 58(C).
    4. Navaz Naghavi & Muhammad Shujaat Mubarik & Devinder Kaur, 2018. "Financial Liberalization And Stock Market Efficiency: Measuring The Threshold Effects Of Governance," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(04), pages 1-24, December.
    5. Amélie Charles & Olivier Darné & Jae H. Kim, 2014. "Precious metals shine? A market efficiency perspective," Working Papers hal-01010516, HAL.
    6. Jasman Tuyon & Zamri Ahmada, 2016. "Behavioural finance perspectives on Malaysian stock market efficiency," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 16(1), pages 43-61, March.
    7. Moews, Ben & Ibikunle, Gbenga, 2020. "Predictive intraday correlations in stable and volatile market environments: Evidence from deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    8. Mostafa Raeisi Sarkandiz & Robabeh Bahlouli, 2019. "The Stock Market between Classical and Behavioral Hypotheses: An Empirical Investigation of the Warsaw Stock Exchange," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 4(2), pages 67-88, December.
    9. Koichiro Moriya & Akihiko Noda, 2023. "On the Time-Varying Structure of the Arbitrage Pricing Theory using the Japanese Sector Indices," Papers 2305.05998, arXiv.org, revised Mar 2024.
    10. Godfrey, Keith R.L., 2017. "Toward a model-free measure of market efficiency," Pacific-Basin Finance Journal, Elsevier, vol. 44(C), pages 97-112.
    11. Hiremath, Gourishankar S & Kumari, Jyoti, 2014. "Stock returns predictability and the adaptive market hypothesis in emerging markets: evidence from India," MPRA Paper 58378, University Library of Munich, Germany.
    12. Hyejung Chung & Kyung-shik Shin, 2018. "Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction," Sustainability, MDPI, vol. 10(10), pages 1-18, October.
    13. Wen-Jun Xue & Li-Wen Zhang, 2016. "Stock Return Autocorrelations and Predictability in the Chinese Stock Market: Evidence from Threshold Quantile Autoregressive Models," Working Papers 1605, Florida International University, Department of Economics.
    14. Bariviera, A.F. & Guercio, M. Belén & Martinez, Lisana B., 2012. "A comparative analysis of the informational efficiency of the fixed income market in seven European countries," Economics Letters, Elsevier, vol. 116(3), pages 426-428.
    15. Akihiko Noda, 2021. "On the evolution of cryptocurrency market efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 28(6), pages 433-439, March.
    16. Pu, Yingjian & Yang, Baochen, 2022. "The commodity futures' historical basis in trading strategy and portfolio investment," Energy Economics, Elsevier, vol. 105(C).
    17. Akash Doshi & Alexander Issa & Puneet Sachdeva & Sina Rafati & Somnath Rakshit, 2020. "Deep Stock Predictions," Papers 2006.04992, arXiv.org.
    18. Charfeddine, Lanouar & Khediri, Karim Ben & Aye, Goodness C. & Gupta, Rangan, 2018. "Time-varying efficiency of developed and emerging bond markets: Evidence from long-spans of historical data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 632-647.
    19. Stefenon, Stefano Frizzo & Seman, Laio Oriel & Aquino, Luiza Scapinello & Coelho, Leandro dos Santos, 2023. "Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants," Energy, Elsevier, vol. 274(C).
    20. David Rushing Dewhurst & Michael Vincent Arnold & Colin Michael Van Oort, 2018. "Selection mechanisms affect volatility in evolving markets," Papers 1812.05657, arXiv.org, revised Apr 2019.

    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:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01801-3. 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.