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Found 7 papers, 1 papers with code
How Robust are Limit Order Book Representations under Data Perturbation?
The success of machine learning models in the financial domain is highly reliant on the quality of the data representation.
Towards Robust Representation of Limit Orders Books for Deep Learning Models
The success of deep learning-based limit order book forecasting models is highly dependent on the quality and the robustness of the input data representation.
Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets
Continuous double auctions such as the limit order book employed by exchanges are widely used in practice to match buyers and sellers of a variety of financial instruments.
A Framework for Institutional Risk Identification using Knowledge Graphs and Automated News Profiling
Organizations around the world face an array of risks impacting their operations globally.
Get Real: Realism Metrics for Robust Limit Order Book Market Simulations
Machine learning (especially reinforcement learning) methods for trading are increasingly reliant on simulation for agent training and testing.
On the Importance of Opponent Modeling in Auction Markets
The dynamics of financial markets are driven by the interactions between participants, as well as the trading mechanisms and regulatory frameworks that govern these interactions.
Compression and Interpretability of Deep Neural Networks via Tucker Tensor Layer: From First Principles to Tensor Valued Back-Propagation
This is achieved through a tensor valued approach, based on the proposed Tucker Tensor Layer (TTL), as an alternative to the dense weight-matrices of DNNs.
