11 papers
Gaussian Boson Sampling for Asset Clustering in Statistical Arbitrage Portfolios
Dayne Marcus Lopena, Daniel Buguks, Zhenghao Li +7
Gaussian Boson Sampling (GBS) provides a native photonic quantum heuristic for sampling dense subgraphs from adjacency matrices, offering a scalable physical approach to combinator…
Data-Driven Graph Filters via Adaptive Spectral Shaping
Dylan Sandfelder, Mihai Cucuringu, Xiaowen Dong
We introduce Adaptive Spectral Shaping, a data-driven framework for graph filtering that learns a reusable baseline spectral kernel and modulates it with a small set of Gaussian fa…
The Market Maker's Dilemma: Navigating the Fill Probability vs. Post-Fill Returns Trade-Off
Jakob Albers, Mihai Cucuringu, Sam Howison +1
Using data from a live trading experiment on the Binance Bitcoin perpetual, we examine the effects of (i) basic order book mechanics and (ii) the persistence of price changes from…
JaxMARL-HFT: GPU-Accelerated Large-Scale Multi-Agent Reinforcement Learning for High-Frequency Trading
Valentin Mohl, Sascha Frey, Reuben Leyland +6
Agent-based modelling (ABM) approaches for high-frequency financial markets are difficult to calibrate and validate, partly due to the large parameter space created by defining fix…
DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets
Wanyun Zhou, Saizhuo Wang, Mihai Cucuringu +5
The lead-lag effect, where the price movement of one asset systematically precedes that of another, has been widely observed in financial markets and conveys valuable predictive si…
On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective
Ning Zhang, Henry Kenlay, Li Zhang +2
Graph convolutional neural networks (GCNNs) have emerged as powerful tools for analyzing graph-structured data, achieving remarkable success across diverse applications. However, t…