1 citations · 2 across the 5 of their papers we have counts for
8 papers
What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books
Fernando Acero
Autoregressive transformers trained on limit order book (LOB) message streams are increasingly proposed as foundation models for market microstructure. Because the generative proce…
Reinforcement Learning with Hawkes-Structured Networks for Market Making
Fernando Acero, Philipp Plank, Yufei Zhang
Market making under realistic order-flow dynamics is challenging due to the clustered, self-exciting nature of order arrivals, which are naturally modeled by non-Markovian Hawkes p…
Diffusion models for dynamic volatility surface generation and data-driven hedging
Yinbin Han, Jack Yuxiang Zhang, Manuel Torres +2
We develop a diffusion-model framework for dynamic implied-volatility surface generation and evaluate its economic usefulness through data-driven hedging. The framework consists of…
Entropy Regularization Improves Policy Robustness in Continuous-Time Reinforcement Learning
Jialun Cao, Fernando Acero, David Šiška +1
Entropy regularization is widely used in continuous-time reinforcement learning (RL) to reduce sensitivity to environmental perturbations, yet its robustness benefits lack a rigoro…
When Do We Need LLMs? A Diagnostic for Language-Driven Bandits
Uljad Berdica, Fernando Acero, Anton Ipsen +3
We study Contextual Multi-Armed Bandits (CMABs) for non-episodic decision-making problems where the context includes both textual and numerical information (e.g., recommendation sy…
Distill and Align Decomposition for Enhanced Claim Verification
Jabez Magomere, Elena Kochkina, Samuel Mensah +6
Complex claim verification requires decomposing sentences into verifiable subclaims, yet existing methods struggle to align decomposition quality with verification performance. We…
Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization
Fernando Acero, Parisa Zehtabi, Nicolas Marchesotti +3
Portfolio optimization involves determining the optimal allocation of portfolio assets in order to maximize a given investment objective. Traditionally, some form of mean-variance…
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
Kyle Mana, Fernando Acero, Stephen Mak +4
Discrete optimization belongs to the set of -hard problems, spanning fields such as mixed-integer programming and combinatorial optimization. A current standard appro…