IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v66y2025i1d10.1007_s10614-024-10714-1.html
   My bibliography  Save this article

Stock Market Trend Prediction Using Deep Learning Approach

Author

Listed:
  • Mahmoud Ahmad Al-Khasawneh

    (Skyline University College, University City Sharjah
    Applied Science Private University
    Jadara University Research Center, Jadara University)

  • Asif Raza

    (Bahauddin Zakariya University)

  • Saif Ur Rehman Khan

    (Central South University)

  • Zia Khan

    (Central South University)

Abstract

Since the dawn of financial market trading, traders have continually sought methods to enhance their predictive capabilities for future price movements. This pursuit is driven by the significant daily trading volumes observed in financial markets worldwide. While traditional econometric and statistical methods have historically dominated in forecasting the behaviors of stock exchanges such as the Pakistan Stock Exchange, there remains a relatively limited exploration into the realm of artificial intelligence (AI) and machine learning (ML) techniques for addressing the inherent unpredictability of these markets. This study aims to improve the accuracy of forecasting the closing index of the Pakistan Stock Exchange by leveraging AI-based models, particularly employing the Deep Learning (DL) Long Short-Term Memory (LSTM) recurrent neural network. These DL models are anticipated to outperform traditional time series methods in predicting market indices. The primary objective of this work is to empower short-term investors with more precise index forecasts, enabling them to make informed and strategic trading decisions through the application of AI-based models.

Suggested Citation

  • Mahmoud Ahmad Al-Khasawneh & Asif Raza & Saif Ur Rehman Khan & Zia Khan, 2025. "Stock Market Trend Prediction Using Deep Learning Approach," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 453-484, July.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10714-1
    DOI: 10.1007/s10614-024-10714-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10714-1
    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/s10614-024-10714-1?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

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Bitanu Chatterjee & Sayan Acharya & Trinav Bhattacharyya & Seyedali Mirjalili & Ram Sarkar, 2023. "Stock market prediction using Altruistic Dragonfly Algorithm," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-20, April.
    3. Farah Naz & Kanwal Zahra & Muhammad Ahmad & Salman Riaz, 2021. "Day-of-the-week effect: A sectoral analysis of Pakistan stock exchange," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 8(02), pages 1-29, June.
    4. Andrey Kudryavtsev, 2020. "Immediate And Longer-Term Stock Price Dynamics Following Large Stock Price Changes," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 15(01), pages 1-17, March.
    5. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    6. Chronopoulos, Dimitris K. & Papadimitriou, Fotios I. & Vlastakis, Nikolaos, 2018. "Information demand and stock return predictability," Journal of International Money and Finance, Elsevier, vol. 80(C), pages 59-74.
    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. Ahad Yaqoob & Syed M. Abdullah, 2025. "Predictive Performance of LSTM Networks on Sectoral Stocks in an Emerging Market: A Case Study of the Pakistan Stock Exchange," Papers 2509.14401, arXiv.org.

    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. Shailesh Rana & William H. Bommer & G. Michael Phillips, 2020. "Predicting Returns for Growth and Value Stocks: A Forecast Assessment Approach Using Global Asset Pricing Models," International Journal of Economics and Financial Issues, Econjournals, vol. 10(4), pages 88-106.
    2. David M. Ritzwoller & Joseph P. Romano, 2019. "Uncertainty in the Hot Hand Fallacy: Detecting Streaky Alternatives to Random Bernoulli Sequences," Papers 1908.01406, arXiv.org, revised Apr 2021.
    3. Shazia Ghani, 2011. "A re-visit to Minsky after 2007 financial meltdown," Post-Print halshs-01027435, HAL.
    4. Lea Steininger & Casimir Hesse, 2024. "Buying into new ideas: The ECB’s evolving justification of unlimited liquidity," Department of Economics Working Papers wuwp357, Vienna University of Economics and Business, Department of Economics.
    5. Christiane Goodfellow & Dirk Schiereck & Steffen Wippler, 2013. "Are behavioural finance equity funds a superior investment? A note on fund performance and market efficiency," Journal of Asset Management, Palgrave Macmillan, vol. 14(2), pages 111-119, April.
    6. Cagli, Efe Caglar & Taskin, Dilvin & Evrim Mandaci, Pınar, 2019. "The short- and long-run efficiency of energy, precious metals, and base metals markets: Evidence from the exponential smooth transition autoregressive models," Energy Economics, Elsevier, vol. 84(C).
    7. Andrew Weinbach & Rodney J. Paul, 2009. "National television coverage and the behavioural bias of bettors: the American college football totals market," International Gambling Studies, Taylor & Francis Journals, vol. 9(1), pages 55-66, April.
    8. Plantinga, Andrew J. & Provencher, Bill, 2001. "Internal Consistency In Models Of Optimal Resource Use Under Uncertainty," 2001 Annual meeting, August 5-8, Chicago, IL 20712, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    9. Growitsch Christian & Nepal Rabindra & Stronzik Marcus, 2015. "Price Convergence and Information Efficiency in German Natural Gas Markets," German Economic Review, De Gruyter, vol. 16(1), pages 87-103, February.
    10. Oxelheim, Lars & Rafferty, Michael, 2005. "On the static efficiency of secondary bond markets," Journal of Multinational Financial Management, Elsevier, vol. 15(2), pages 117-135, April.
    11. Daniele Giachini & Shabnam Mousavi & Matteo Ottaviani, 2025. "From zero-intelligence to Bayesian learning: the effect of rationality on market efficiency," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 20(3), pages 659-676, July.
    12. Baoqiang Zhan & Shu Zhang & Helen S. Du & Xiaoguang Yang, 2022. "Exploring Statistical Arbitrage Opportunities Using Machine Learning Strategy," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 861-882, October.
    13. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    14. Gaio, Luiz Eduardo & Stefanelli, Nelson Oliveira & Pimenta, Tabajara & Bonacim, Carlos Alberto Grespan & Gatsios, Rafael Confetti, 2022. "The impact of the Russia-Ukraine conflict on market efficiency: Evidence for the developed stock market," Finance Research Letters, Elsevier, vol. 50(C).
    15. Anastasios Evgenidis & Stephanos Papadamou, 2021. "The impact of unconventional monetary policy in the euro area. Structural and scenario analysis from a Bayesian VAR," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5684-5703, October.
    16. Nuruddeen Usman & Kodili Nwanneka & Nduka, 2023. "Announcement Effect of COVID-19 on Cryptocurrencies," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 3(3), pages 1-4.
    17. Olayemi O Adu & Blessing O Idakwoji, 2024. "Commodity Market Efficiency - New Evidence From the Russia-Ukraine War," Energy RESEARCH LETTERS, Asia-Pacific Applied Economics Association, vol. 5(2), pages 1-6.
    18. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    19. Carol Alexander & Anca Dimitriu, 2003. "Equity Indexing: Conitegration and Stock Price Dispersion: A Regime Switiching Approach to market Efficiency," ICMA Centre Discussion Papers in Finance icma-dp2003-02, Henley Business School, University of Reading.
    20. Robert C. Merton, 2006. "Paul Samuelson and Financial Economics," The American Economist, Sage Publications, vol. 50(2), pages 9-31, October.

    More about this item

    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:kap:compec:v:66:y:2025:i:1:d:10.1007_s10614-024-10714-1. 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.