Corpus ID: 236034106

Predicting Daily Trading Volume via Various Hidden States

@article{Ma2021PredictingDT,
  title={Predicting Daily Trading Volume via Various Hidden States},
  author={Shaojun Ma and Pengcheng Li},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.07678}
}
  • Shaojun Ma, Pengcheng Li
  • Published 2021
  • Computer Science, Economics
  • ArXiv
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… Expand

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