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

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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.

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Acknowledgements

We would like to express our gratitude to Ao Li, an undergraduate student from the School of Computer Science and Technology at Anyang Institute of Technology, for providing assistance in LSTM modeling and visualizing the research results.

Funding

This research received no external funding.

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Authors and Affiliations

  1. Graduate School of International Studies, Ewha Womans University, Seoul, Republic of Korea

    Xiaoxiao Liu

  2. School of Computer Science and Technology, Anyang Institute of Technology, Anyang, Henan, China

    Wei Wang

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  1. Xiaoxiao Liu
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  2. Wei Wang
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Contributions

Conceptualization, investigation, methodology, data curation, validation, writing—original draft preparation, and writing—review and editing were carried out by Xiaoxiao Liu; software and resources required for computation were provided by Wei Wang. All authors have reviewed and approved the final version of the manuscript for publication.

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Correspondence to Wei Wang.

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Appendix 1

Appendix 1

See Tables 4, 5, 6, 7, 8, 9.

Table 4 VAR Lag Order Selection Criteria for American SP500
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Table 5 VAR Lag Order Selection Criteria for British FTSE
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Table 6 VAR Lag Order Selection Criteria for French FCHI
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Table 7 VAR lag order selection criteria for German GDAXI
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Table 8 VAR lag order selection criteria for Hong Kong HSI
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Table 9 VAR lag order selection criteria for Japanese N225
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Liu, X., Wang, W. Improving Sliding Window Effect of LSTM in Stock Prediction Based on Econometrics Theory. Comput Econ (2024). https://doi.org/10.1007/s10614-024-10627-z

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