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A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift
Defu Cao · Yousef El-Laham · Loc Trinh · Svitlana Vyetrenko · Yan Liu
Event URL: https://openreview.net/forum?id=_u-1--wBV1 »

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.

Author Information

Defu Cao (University of Southern California)

Cao is primarily interested in developing machine learning and data mining algorithms that demonstrate a deep understanding of the world with special structures, including time series, spatio-temporal data, and relational data. To this end, his research aims to integrate causal inference, graph neural networks, spectral domain representation, interpretability, and robustness, he is also interested in multi-task learning and pre-training model in the NLP domain. He has published his research in top conference proceedings including NeurIPS, ICRA, ICDM, PAKDD, NAACL, and TrustCom.

Yousef El-Laham (J.P. Morgan Chase)
Loc Trinh (University of Southern California)
Svitlana Vyetrenko (J. P. Morgan, Artificial Intelligence Research)
Yan Liu (University of Southern California)

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