IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v65y2025i4d10.1007_s10614-024-10627-z.html
   My bibliography  Save this article

Improving Sliding Window Effect of LSTM in Stock Prediction Based on Econometrics Theory

Author

Listed:
  • Xiaoxiao Liu

    (Ewha Womans University)

  • Wei Wang

    (Anyang Institute of Technology)

Abstract

This study examines the influence of the sliding window in the LSTM model on its predictive performance in the stock market. The investigation encompasses three aspects: the impact of the stationarity of the original data, the effect of the time interval, and the influence of the input order of data. Additionally, a standard VAR model is established for a comparative benchmark. The experimental dataset comprises the daily stock index prices of the six major stock markets from the January 2010 to December 2019. The experimental results demonstrate that stationary input data enhances the predictive performance of the LSTM model. Furthermore, shorter time interval tends to yield improved outcomes, while the order of input data does not impact the performance of the LSTM. Although the predictive capability of the LSTM model may not consistently surpass that of the standard VAR model, which is different from the previous research, it serves to compensate for the conditional limitations associated with VAR model construction.

Suggested Citation

  • Xiaoxiao Liu & Wei Wang, 2025. "Improving Sliding Window Effect of LSTM in Stock Prediction Based on Econometrics Theory," Computational Economics, Springer;Society for Computational Economics, vol. 65(4), pages 2057-2080, April.
  • Handle: RePEc:kap:compec:v:65:y:2025:i:4:d:10.1007_s10614-024-10627-z
    DOI: 10.1007/s10614-024-10627-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-024-10627-z
    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/s10614-024-10627-z?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. Christou, Christina & Cunado, Juncal & Gupta, Rangan & Hassapis, Christis, 2017. "Economic policy uncertainty and stock market returns in PacificRim countries: Evidence based on a Bayesian panel VAR model," Journal of Multinational Financial Management, Elsevier, vol. 40(C), pages 92-102.
    2. Allen, David E. & Amram, Ron & McAleer, Michael, 2013. "Volatility spillovers from the Chinese stock market to economic neighbours," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 238-257.
    3. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    4. Zhifang He & Linjie He & Fenghua Wen, 2019. "Risk Compensation and Market Returns: The Role of Investor Sentiment in the Stock Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(3), pages 704-718, February.
    5. Manel Youssef & Khaled Mokni & Ahdi Noomen Ajmi, 2021. "Dynamic connectedness between stock markets in the presence of the COVID-19 pandemic: does economic policy uncertainty matter?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-27, December.
    6. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    7. Dopke, Jorg & Pierdzioch, Christian, 2006. "Politics and the stock market: Evidence from Germany," European Journal of Political Economy, Elsevier, vol. 22(4), pages 925-943, December.
    8. José Curto & José Pinto & Gonçalo Tavares, 2009. "Modeling stock markets’ volatility using GARCH models with Normal, Student’s t and stable Paretian distributions," Statistical Papers, Springer, vol. 50(2), pages 311-321, March.
    9. repec:taf:emfitr:v:55:y:2019:i:3:p:704-718 is not listed on IDEAS
    10. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    11. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    12. Lin, Yu & Yan, Yan & Xu, Jiali & Liao, Ying & Ma, Feng, 2021. "Forecasting stock index price using the CEEMDAN-LSTM model," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    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. M. T. Alguacil & V. Orts, 2003. "Inward Foreign Direct Investment and Imports in Spain," International Economic Journal, Taylor & Francis Journals, vol. 17(3), pages 19-38.
    2. Chu, Kam Hon, 2010. "Bank mergers, branch networks and economic growth: Theory and evidence from Canada, 1889-1926," Journal of Macroeconomics, Elsevier, vol. 32(1), pages 265-283, March.
    3. Xiaojie Xu, 2017. "Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs," Empirical Economics, Springer, vol. 52(2), pages 731-758, March.
    4. Dizaji, Sajjad Faraji, 2014. "The effects of oil shocks on government expenditures and government revenues nexus (with an application to Iran's sanctions)," Economic Modelling, Elsevier, vol. 40(C), pages 299-313.
    5. Maurizio Baussola, 2000. "The Causality Between R&D And Investment," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 9(4), pages 385-399.
    6. Olagunju, Kehinde Oluseyi & Feng, Siyi & Patton, Myles, 2021. "Dynamic relationships among phosphate rock, fertilisers and agricultural commodity markets: Evidence from a vector error correction model and Directed Acyclic Graphs," Resources Policy, Elsevier, vol. 74(C).
    7. Johannes W. Fedderke, 2022. "Identifying supply and demand shocks in the South African Economy, 1960–2020," South African Journal of Economics, Economic Society of South Africa, vol. 90(3), pages 349-389, September.
    8. Julia Campos & Neil R. Ericsson & David F. Hendry, 2005. "General-to-specific modeling: an overview and selected bibliography," International Finance Discussion Papers 838, Board of Governors of the Federal Reserve System (U.S.).
    9. Kenneth W. Clements & Patricia Wang, 2003. "Who Cites What?," The Economic Record, The Economic Society of Australia, vol. 79(245), pages 229-244, June.
    10. Zhou, Kaile & Hu, Dingding & Li, Fangyi, 2022. "Impact of COVID-19 on private driving behavior: Evidence from electric vehicle charging data," Transport Policy, Elsevier, vol. 125(C), pages 164-178.
    11. Maslov, Alexander, 2011. "Inflationary Handicap Of The Monetary Transmission Mechanism: Evidence From Russia," MPRA Paper 50036, University Library of Munich, Germany, revised 12 Apr 2012.
    12. Nieh, Chien-Chung & Yau, Hwey-Yun, 2004. "Time series analysis for the interest rates relationships among China, Hong Kong, and Taiwan money markets," Journal of Asian Economics, Elsevier, vol. 15(1), pages 171-188, February.
    13. Adusei Jumah & Sohbet Karbuz & Gerhard Runstler, 1999. "Interest rate differentials, market integration, and the efficiency of commodity futures markets," Applied Financial Economics, Taylor & Francis Journals, vol. 9(1), pages 101-108.
    14. José María Gil & J. Clemente & A, Montañés & M. Reyes, 1996. "Integración espacial y cointegración: una aplicación al mercado de cereales en España," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 6, pages 103-130, Diciembre.
    15. Katsuya Ito, 2008. "Oil price and macroeconomy in Russia," Economics Bulletin, AccessEcon, vol. 17(17), pages 1-9.
    16. Sa-ngasoongsong, Akkarapol & Bukkapatnam, Satish T.S. & Kim, Jaebeom & Iyer, Parameshwaran S. & Suresh, R.P., 2012. "Multi-step sales forecasting in automotive industry based on structural relationship identification," International Journal of Production Economics, Elsevier, vol. 140(2), pages 875-887.
    17. Michael S. Haigh & Nikos K. Nomikos & David A. Bessler, 2004. "Integration and Causality in International Freight Markets: Modeling with Error Correction and Directed Acyclic Graphs," Southern Economic Journal, John Wiley & Sons, vol. 71(1), pages 145-162, July.
    18. Rajesh Mohnot, 2020. "Examining Granger Causality in the Behavioral Reactions of Institutional Investors— Evidence from India," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(04), pages 1-21, January.
    19. Xu, Haifeng & Hamori, Shigeyuki, 2012. "Dynamic linkages of stock prices between the BRICs and the United States: Effects of the 2008–09 financial crisis," Journal of Asian Economics, Elsevier, vol. 23(4), pages 344-352.
    20. Panayiotis Diamandis & Georgios Kouretas, 1995. "Cointegration and market efficiency: a time series analysis of the Greek drachma," Applied Economics Letters, Taylor & Francis Journals, vol. 2(8), pages 271-277.

    More about this item

    Keywords

    Long short-term memory; Sliding window; Vector autoregression model; Stock index price forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    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:65:y:2025:i:4:d:10.1007_s10614-024-10627-z. 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.