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Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network

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Listed:
  • Shankhajyoti De

    (IIT Guwahati)

  • Arabin Kumar Dey

    (IIT Guwahati)

  • Deepak Kumar Gouda

    (IIT Guwahati)

Abstract

In this paper, we show an innovative way to construct bootstrap confidence interval of a signal estimated based on a univariate LSTM model. We take three different types of bootstrap methods for dependent set up. We prescribe some useful suggestions to select the optimal block length while performing the bootstrapping of the sample. We also propose a benchmark to compare the confidence interval measured through different bootstrap strategies. We illustrate the experimental results through some stock price data set.

Suggested Citation

  • Shankhajyoti De & Arabin Kumar Dey & Deepak Kumar Gouda, 2022. "Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network," Annals of Data Science, Springer, vol. 9(2), pages 271-284, April.
    • Handle: RePEc:spr:aodasc:v:9:y:2022:i:2:d:10.1007_s40745-020-00307-8
    DOI: 10.1007/s40745-020-00307-8
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    References listed on IDEAS

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    1. Dharmaraja Selvamuthu & Vineet Kumar & Abhishek Mishra, 2019. "Indian stock market prediction using artificial neural networks on tick data," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-12, December.
    2. Shi, Yong & Tang, Ye-ran & Long, Wen, 2019. "Sentiment contagion analysis of interacting investors: Evidence from China’s stock forum," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 246-259.
    3. O F Demirel & T R Willemain, 2002. "Generation of simulation input scenarios using bootstrap methods," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(1), pages 69-78, January.
    4. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
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