Search Results for author:
Found 11 papers, 7 papers with code
HLOB -- Information Persistence and Structure in Limit Order Books
We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it `HLOB'.
Deep Limit Order Book Forecasting
We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange.
Topological Portfolio Selection and Optimization
Following the seminal work of Markowitz, optimal asset allocation can be computed using a constrained optimization model based on empirical covariance.
Homological Convolutional Neural Networks
Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e. g., image, audio, and text data).
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity
The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computational complexity, energy efficiency and interpretability.
FTX's downfall and Binance's consolidation: The fragility of centralised digital finance
This paper investigates the causes of the FTX digital currency exchange's failure in November 2022.
Topological Feature Selection
In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations.
Anatomy of a Stablecoin's failure: the Terra-Luna case
We quantitatively describe the main events that led to the Terra project's failure in May 2022.
Dependency structures in cryptocurrency market from high to low frequency
We investigate logarithmic price returns cross-correlations at different time horizons for a set of 25 liquid cryptocurrencies traded on the FTX digital currency exchange.
Deep Reinforcement Learning for Active High Frequency Trading
The training is performed on three contiguous months of high frequency Limit Order Book data, of which the last month constitutes the validation data.
Deep Learning modeling of Limit Order Book: a comparative perspective
The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading.
