Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods
Abstract
Managing the prediction of metrics in high-frequency financial markets is a challenging task. An efficient way is by monitoring the dynamics of a limit order book to identify the information edge. This paper describes the first publicly available benchmark dataset of high-frequency limit order markets for mid-price prediction. We extracted normalized data representations of time series data for five stocks from the NASDAQ Nordic stock market for a time period of ten consecutive days, leading to a dataset of ~4,000,000 time series samples in total. A day-based anchored cross-validation experimental protocol is also provided that can be used as a benchmark for comparing the performance of state-of-the-art methodologies. Performance of baseline approaches are also provided to facilitate experimental comparisons. We expect that such a large-scale dataset can serve as a testbed for devising novel solutions of expert systems for high-frequency limit order book data analysis.
Keywords
Cite
@article{arxiv.1705.03233,
title = {Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods},
author = {Adamantios Ntakaris and Martin Magris and Juho Kanniainen and Moncef Gabbouj and Alexandros Iosifidis},
journal= {arXiv preprint arXiv:1705.03233},
year = {2020}
}
Comments
Published: Journal of Forecasting