6 citations · 6 across the 15 of their papers we have counts for
15 papers
Feasible Multi-Asset Optimal Execution under Cash Constraints
Ryuji Hashimoto, Namid R. Stillman
Optimal execution (OE) in multi-asset settings involves complex interactions across assets, particularly through shared capital constraints during portfolio rebalancing. While exis…
Financial Tail Risk Beyond Lipschitz Continuity via Semi-Discrete Optimal Transport
Ryan M. Engel, Kibaek Lee, Namid Stillman
Financial returns are heavy-tailed, and accurate tail risk estimation is central to portfolio risk management. Modern neural generators sample by pushing a simple base distribution…
FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching
Zhuohan Wang, Andreea Bacalum, Ollie Olby +2
Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation…
LOB-ID: Evaluating Synthetic Market Data by Inception Distances
Andreea Bacalum, Zhuohan Wang, Ollie Olby +2
Generative models of limit orderbook (LOB) data have advanced rapidly, but their evaluation often focuses on stylised facts and selected market statistics. These measures provide u…
TABL-ABM: A Hybrid Framework for Synthetic LOB Generation
Ollie Olby, Rory Baggott, Namid Stillman
The recent application of deep learning models to financial trading has heightened the need for high fidelity financial time series data. This synthetic data can be used to supplem…
Right Place, Right Time: Market Simulation-based RL for Execution Optimisation
Ollie Olby, Andreea Bacalum, Rory Baggott +1
Execution algorithms are vital to modern trading, they enable market participants to execute large orders while minimising market impact and transaction costs. As these algorithms…
To Trade or Not to Trade: An Agentic Approach to Estimating Market Risk Improves Trading Decisions
Dimitrios Emmanoulopoulos, Ollie Olby, Justin Lyon +1
Large language models (LLMs) are increasingly deployed in agentic frameworks, in which prompts trigger complex tool-based analysis in pursuit of a goal. While these frameworks have…
Agent-based Liquidity Risk Modelling for Financial Markets
Perukrishnen Vytelingum, Rory Baggott, Namid Stillman +4
In this paper, we describe a novel agent-based approach for modelling the transaction cost of buying or selling an asset in financial markets, e.g., to liquidate a large position a…
Neuro-Symbolic Traders: Assessing the Wisdom of AI Crowds in Markets
Namid R. Stillman, Rory Baggott
Deep generative models are becoming increasingly used as tools for financial analysis. However, it is unclear how these models will influence financial markets, especially when the…
Deep Calibration of Market Simulations using Neural Density Estimators and Embedding Networks
Namid R. Stillman, Rory Baggott, Justin Lyon +4
The ability to construct a realistic simulator of financial exchanges, including reproducing the dynamics of the limit order book, can give insight into many counterfactual scenari…
Deeper Hedging: A New Agent-based Model for Effective Deep Hedging
Kang Gao, Stephen Weston, Perukrishnen Vytelingum +3
We propose the Chiarella-Heston model, a new agent-based model for improving the effectiveness of deep hedging strategies. This model includes momentum traders, fundamental traders…
Graph-informed simulation-based inference for models of active matter
Namid R. Stillman, Silke Henkes, Roberto Mayor +1
Many collective systems exist in nature far from equilibrium, ranging from cellular sheets up to flocks of birds. These systems reflect a form of active matter, whereby individual…
Towards open-ended evolutionary simulator for developing novel tumour drug delivery systems
Igor Balaz, Tara Petric, Namid Stillman
Tumours behave as moving targets that can evade chemotherapeutic treatments by rapidly acquiring resistance via various mechanisms. In Balaz et al. (2021, Biosystems; 199:104290) w…
Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment
Namid Stillman, Igor Balaz, Antisthenis Tsompanas +5
We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. EVONANO includes a simulator to grow tumours, extract representative…
Model Exploration with Cost-Aware Learning
Namid Stillman, Igor Balazs, Sabine Hauert
We present an extension to active learning routines in which non-constant costs are explicitly considered. This work considers both known and unknown costs and introduces the term…