---
title: High-Frequency Trading Volume Prediction with Neural Networks
url: https://www.ml-quant.com/papers/repec/kap-fmktpm-v-37-y-2023-i-2-d-10-1007-s11408-022-00421-y/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:kap:fmktpm:v:37:y:2023:i:2:d:10.1007_s11408-022-00421-y
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11408-022-00421-y%3Bh%3Drepec%3Akap%3Afmktpm%3Av%3A37%3Ay%3A2023%3Ai%3A2%3Ad%3A10.1007_s11408-022-00421-y
featured: 2023-07-12
citations: unknown
topic: Trading, Microstructure & Execution
---


# High-Frequency Trading Volume Prediction with Neural Networks

A study successfully used a neural network to predict trading volumes of the CSI300 futures index using short-term data, finding that adding additional data did not improve predictions.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Flink.springer.com%2F10.1007%2Fs11408-022-00421-y%3Bh%3Drepec%3Akap%3Afmktpm%3Av%3A37%3Ay%3A2023%3Ai%3A2%3Ad%3A10.1007_s11408-022-00421-y
- Identifier: RePEc:kap:fmktpm:v:37:y:2023:i:2:d:10.1007_s11408-022-00421-y
- Released: 2023-07-12
- First featured: Quant Letter No. 7 (2023-07-12): https://www.ml-quant.com/issues/2023-07-12/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Trading, Microstructure & Execution

## Related

- [Hybrid Neural Network for High-Frequency Stock Trading](https://www.ml-quant.com/papers/repec/gam-jecnmx-v-11-y-2023-i-2-p-13-d-1149628/): New online time series forecasting method for high-frequency trading using deep learning models.
- [FAST: Efficient Action Tokenization for Vision-Language-Action Models](https://www.ml-quant.com/papers/arxiv/2501.09747/): A new tokenization scheme, Frequency-space Action Sequence Tokenization (FAST), has been proposed for robot actions, facilitating the training of vision-language action policies for complex and high-frequency tasks.
- [Deep Reinforcement Learning for Active High Frequency Trading](https://www.ml-quant.com/papers/arxiv/2101.07107/): A new Deep Reinforcement Learning framework has been developed for high frequency stock trading, showing potential for profitable long-term strategies.
- [Neural Networks Can Detect Model-Free Static Arbitrage Strategies](https://www.ml-quant.com/papers/arxiv/2306.16422/): Neural networks can identify model-free static arbitrage opportunities in financial markets with many traded securities, offering tractability and effectiveness.
- [Estimation of an Order Book Dependent Hawkes Process for Large Datasets](https://www.ml-quant.com/papers/arxiv/2307.09077/): A new high-frequency trading model uses a Hawkes process and high-dimensional functions from the order book, capable of handling billions of data points and tested on four NYSE stocks.
- [Approximately optimal trade execution strategies under fast mean-reversion](https://www.ml-quant.com/papers/arxiv/2307.07024/): A study models market quality using uncertain volatility and liquidity, studying optimal strategy approximations and providing estimation methods for the model using high-frequency data.