Deep Learning modeling of Limit Order Book: a comparative perspective
This work provides insights for financial traders and researchers by evaluating model performance on limit order book prediction, though it is incremental as it focuses on comparative analysis without introducing new methods.
The study compared various deep learning models for high-frequency trading on limit order book data, finding that Multilayer Perceptrons performed similarly or better than state-of-the-art CNN-LSTM architectures, suggesting that dynamic spatial and temporal dimensions are a good approximation but not necessarily the true underlying dimensions of the LOB.
The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature space and dataset, and then clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. We observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.