Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling

Zhao, Liang; Li, Wei; Bao, Ruihan; Harimoto, Keiko; Wu, Yunfang; Sun, Xu

doi:10.24963/ijcai.2021/518

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…Besides, temporal mixture ensemble models [1], Bayesian auto-regressive models [12] and graph neural networks [33] are also explored in volume prediction. [32] train a Transformer model [30] with adversarial objectives to improve the model performance and robustness at the same time.…”

Section: Volume Predictionmentioning

confidence: 99%

Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction

Li¹,

Zhang²,

Bao³

et al. 2022

Preprint

Self Cite

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Traditional knowledge distillation in classification problems transfers the knowledge via class correlations in the soft label produced by teacher models, which are not available in regression problems like stock trading volume prediction. To remedy this, we present a novel distillation framework for training a light-weight student model to perform trading volume prediction given historical transaction data. Specifically, we turn the regression model into a probabilistic forecasting model, by training models to predict a Gaussian distribution to which the trading volume belongs. The student model can thus learn from the teacher at a more informative distributional level, by matching its predicted distributions to that of the teacher. Two correlational distillation objectives are further introduced to encourage the student to produce consistent pairwise relationships with the teacher model. We evaluate the framework on a real-world stock volume dataset with two different time window settings. Experiments demonstrate that our framework is superior to strong baseline models, compressing the model size by 5× while maintaining 99.6% prediction accuracy. The extensive analysis further reveals that our framework is more effective than vanilla distillation methods under low-resource scenarios. §

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Section: Volume Predictionmentioning

confidence: 99%

Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction

Li¹,

Zhang²,

Bao³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…(2020b) in predicting prices of stocks through encoder/decoder LSTM models. Zhao et al . (2021) have considered different machine learning models that include a support vector machine, a random forest and an LSTM, and a graph-based method for predicting trading volumes' movement patterns by using predictive information from prices of stocks.…”

Section: Introductionmentioning

confidence: 99%

“…(2021) have considered different machine learning models that include a support vector machine, a random forest and an LSTM, and a graph-based method for predicting trading volumes' movement patterns by using predictive information from prices of stocks. Zhao et al . (2021) have determined that the graph-based method leads to higher prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

High-frequency CSI300 futures trading volume predicting through the neural network

Xu,

Zhang

2023

AJEB

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PurposeFor policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.Design/methodology/approachIn order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?FindingsThe results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.Originality/valueThe results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

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Distributional Correlation–Aware Knowledge Distillation for Stock Trading Volume Prediction

Zhang

Bao

et al. 2023

Machine Learning and Knowledge Discovery in Databases

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Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling

Cited by 7 publications

References 3 publications

Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction

Distributional Correlation--Aware Knowledge Distillation for Stock Trading Volume Prediction

High-frequency CSI300 futures trading volume predicting through the neural network

Distributional Correlation–Aware Knowledge Distillation for Stock Trading Volume Prediction

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