Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods

Adamantios Ntakaris, Martin Magris, Juho Kanniainen, Moncef Gabbouj and Alexandros Iosifidis

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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.

Date: 2017-05, Revised 2020-03
New Economics Papers: this item is included in nep-mst
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