207 citations · 297 across the 14 of their papers we have counts for
15 papers
Dependence-Informed Sparse Neural Architecture for Stock Return Prediction
Hongyu Lin, Yulin Chen, Yuanrong Wang +2
Using neural networks for stock return prediction typically requires choices about depth and hidden-layer width that are difficult to connect to financial interpretation. We study…
Compositional Sparsity as an Inductive Bias for Neural Architecture Design
Hongyu Lin, Antonio Briola, Yuanrong Wang +1
Identifying the structural priors that enable Deep Neural Networks (DNNs) to overcome the curse of dimensionality is a fundamental challenge in machine learning theory. Existing li…
Graph Regularized PCA
Antonio Briola, Marwin Schmidt, Fabio Caccioli +4
Multivariate data often exhibit complex dependencies that violate the assumption of isotropic residual noise. For such cases, we introduce Graph Regularized PCA (GR-PCA). It is a g…
Cryptocurrencies in the Balance Sheet: Insights from (Micro)Strategy -- Bitcoin Interactions
Sabrina Aufiero, Antonio Briola, Tesfaye Salarin +3
This paper investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a…
HLOB -- Information Persistence and Structure in Limit Order Books
Antonio Briola, Silvia Bartolucci, Tomaso Aste
We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it `HLOB'. This architecture (i) exploits the information encod…
Deep Limit Order Book Forecasting
Antonio Briola, Silvia Bartolucci, Tomaso Aste
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 t…
Topological Portfolio Selection and Optimization
Yuanrong Wang, Antonio Briola, Tomaso Aste
Modern portfolio optimization is centered around creating a low-risk portfolio with extensive asset diversification. Following the seminal work of Markowitz, optimal asset allocati…
Homological Convolutional Neural Networks
Antonio Briola, Yuanrong Wang, Silvia Bartolucci +1
Deep learning methods have demonstrated outstanding performances on classification and regression tasks on homogeneous data types (e.g., image, audio, and text data). However, tabu…
Homological Neural Networks: A Sparse Architecture for Multivariate Complexity
Yuanrong Wang, Antonio Briola, Tomaso Aste
The rapid progress of Artificial Intelligence research came with the development of increasingly complex deep learning models, leading to growing challenges in terms of computation…
FTX's downfall and Binance's consolidation: The fragility of centralised digital finance
David Vidal-Tomás, Antonio Briola, Tomaso Aste
This paper investigates the causes of the FTX digital currency exchange's failure in November 2022. We identify the collapse of the Terra-Luna ecosystem as the pivotal event that t…
Topological Feature Selection
Antonio Briola, Tomaso Aste
In this paper, we introduce a novel unsupervised, graph-based filter feature selection technique which exploits the power of topologically constrained network representations. We m…
Anatomy of a Stablecoin's failure: the Terra-Luna case
Antonio Briola, David Vidal-Tomás, Yuanrong Wang +1
We quantitatively describe the main events that led to the Terra project's failure in May 2022. We first review, in a systematic way, news from heterogeneous social media sources;…
Dependency structures in cryptocurrency market from high to low frequency
Antonio Briola, Tomaso Aste
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. We study…
Deep Reinforcement Learning for Active High Frequency Trading
Antonio Briola, Jeremy Turiel, Riccardo Marcaccioli +2
We introduce the first end-to-end Deep Reinforcement Learning (DRL) based framework for active high frequency trading in the stock market. We train DRL agents to trade one unit of…
Deep Learning modeling of Limit Order Book: a comparative perspective
Antonio Briola, Jeremy Turiel, Tomaso Aste
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 Re…