A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional ShiftDownload PDF

05 Oct 2022, 00:13 (modified: 16 Nov 2022, 05:45)NeurIPS 2022 Workshop DistShift PosterReaders: Everyone
Keywords: Forecasting, Limit Order Book Dataset, Benchmark, Distributional shifts
TL;DR: This work builds a synthetic financial LOB dataset which has distributional shift after some random shocks happen and can be used to verify machine learning benchmarks for financial trend prediction tasks.
Abstract: In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribution (OOD) LOB data is challenging since distributional shifts are unlabeled in current publicly available LOB datasets. Therefore, it is critical to build a synthetic LOB dataset with labeled OOD samples serving as a testbed for developing models that generalize well to unseen scenarios. In this work, we utilize a multi-agent market simulator to build a synthetic LOB dataset with and without market stress scenarios, which allows for the design of controlled distributional shift benchmarking. Using the proposed synthetic dataset, we provide a holistic analysis on the forecasting performance of three different state-of-the-art forecasting methods. Our results reflect the need for increased researcher efforts to develop algorithms with robustness to distributional shifts in high-frequency time series data.
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