Quantum Weighted Moving Average for Predicting Limit Order Book Trends
Abstract
Can quantum computers be useful for forecasting multivariate financial time series? In this work, we consider the problem of predicting price trends from limit order book (LOB) data. After identifying key components of classical models, we introduce the quantum weighted moving average (QWMA) model. The two main building blocks are, first, classically preprocessing via normalization of both feature and temporal dimensions and, second, a linear combination of unitaries-based layer for unitarily-embedded classical data. The models are evaluated on the FI-2010 benchmark dataset and a second dataset of China A-share stocks. While we do not present evidence of quantum advantage, the combined classical-quantum model demonstrates performances close to the best classical models. The quantum part of the model is expressive enough to focus on the most predictive parts of the time series. Several specializations of the QWMA model are considered, in particular a variant related to the widely-used exponential moving average (EMA). We give consideration to the limitations of the methods and the datasets.
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BibTeX
@misc{kamm2026quantum,
title = {{Quantum Weighted Moving Average for Predicting Limit Order Book Trends}},
author = {Matthias Kamm and Dinh-Long Vu and Patrick Rebentrost},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.01524},
eprint = {2609.01524},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.01524},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum Weighted Moving Average for Predicting Limit Order Book Trends
AU - Matthias Kamm
AU - Dinh-Long Vu
AU - Patrick Rebentrost
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.01524
UR - https://arxiv.org/abs/2609.01524
AB - Can quantum computers be useful for forecasting multivariate financial time series? In this work, we consider the problem of predicting price trends from limit order book (LOB) data. After identifying key components of classical models, we introduce the quantum weighted moving average (QWMA) model. The two main building blocks are, first, classically preprocessing via normalization of both feature and temporal dimensions and, second, a linear combination of unitaries-based layer for unitarily-embedded classical data. The models are evaluated on the FI-2010 benchmark dataset and a second dataset of China A-share stocks. While we do not present evidence of quantum advantage, the combined classical-quantum model demonstrates performances close to the best classical models. The quantum part of the model is expressive enough to focus on the most predictive parts of the time series. Several specializations of the QWMA model are considered, in particular a variant related to the widely-used exponential moving average (EMA). We give consideration to the limitations of the methods and the datasets.
ER -