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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
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    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

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