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Predicting Daily Trading Volume via Various Hidden States

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  • Shaojun Ma
  • Pengcheng Li

Abstract

Predicting intraday trading volume plays an important role in trading alpha research. Existing methods such as rolling means(RM) and a two-states based Kalman Filtering method have been presented in this topic. We extend two states into various states in Kalman Filter framework to improve the accuracy of prediction. Specifically, for different stocks we utilize cross validation and determine best states number by minimizing mean squared error of the trading volume. We demonstrate the effectivity of our method through a series of comparison experiments and numerical analysis.

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  • Shaojun Ma & Pengcheng Li, 2021. "Predicting Daily Trading Volume via Various Hidden States," Papers 2107.07678, arXiv.org.
    • Handle: RePEc:arx:papers:2107.07678
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    File URL: http://arxiv.org/pdf/2107.07678
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    References listed on IDEAS

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    1. Christian T. Brownlees & Fabrizio Cipollini & Giampiero M. Gallo, 2011. "Intra-daily Volume Modeling and Prediction for Algorithmic Trading," Journal of Financial Econometrics, Oxford University Press, vol. 9(3), pages 489-518, Summer.
    2. Ajinkya, Bipin B. & Jain, Prem C., 1989. "The behavior of daily stock market trading volume," Journal of Accounting and Economics, Elsevier, vol. 11(4), pages 331-359, November.
    3. R. H. Shumway & D. S. Stoffer, 1982. "An Approach To Time Series Smoothing And Forecasting Using The Em Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 3(4), pages 253-264, July.
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    Cited by:

    1. Xiaojie Xu & Yun Zhang, 2023. "Neural network predictions of the high-frequency CSI300 first distant futures trading volume," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(2), pages 191-207, June.
    2. Xiaojie Xu & Yun Zhang, 2022. "Forecasting the total market value of a shares traded in the Shenzhen stock exchange via the neural network," Economics Bulletin, AccessEcon, vol. 42(3), pages 1266-1279.

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