10 citations · 29 across the 25 of their papers we have counts for
30 papers
20,000 Rotation Periods in Nearby Open Clusters and Moving Groups from ZTF and ASAS-SN
Yuxi Lu
Open clusters provide uniquely powerful laboratories for studying stellar evolution because their members share a common age and chemical composition. Stellar rotation of low-mass…
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…
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…
Forecasting Intraday Volume in Equity Markets with Machine Learning
Mihai Cucuringu, Kang Li, Chao Zhang
This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the curr…
ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In the rapidly evolving world of financial markets, understanding the dynamics of limit order book (LOB) is crucial for unraveling market microstructure and participant behavior. W…
Tactical Asset Allocation with Macroeconomic Regime Detection
Daniel Cunha Oliveira, Dylan Sandfelder, André Fujita +2
This paper extends the tactical asset allocation literature by incorporating regime modeling using techniques from machine learning. We propose a novel model that classifies curren…
Generalized Factor Neural Network Model for High-dimensional Regression
Zichuan Guo, Mihai Cucuringu, Alexander Y. Shestopaloff
We tackle the challenges of modeling high-dimensional data sets, particularly those with latent low-dimensional structures hidden within complex, non-linear, and noisy relationship…
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…
A GCN-LSTM Approach for ES-mini and VX Futures Forecasting
Nikolas Michael, Mihai Cucuringu, Sam Howison
We propose a novel data-driven network framework for forecasting problems related to E-mini S\&P 500 and CBOE Volatility Index futures, in which products with different expirations…
Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other. Uncovering such relationsh…
Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects
Chao Zhang, Xingyue Pu, Mihai Cucuringu +1
We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks…
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series…
OFTER: An Online Pipeline for Time Series Forecasting
Nikolas Michael, Mihai Cucuringu, Sam Howison
We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series. OFTER utilizes the non-parametric models of k-nearest neighbors and Generali…
DeFi: data-driven characterisation of Uniswap v3 ecosystem & an ideal crypto law for liquidity pools
Deborah Miori, Mihai Cucuringu
Uniswap is a Constant Product Market Maker built around liquidity pools, where pairs of tokens are exchanged subject to a fee that is proportional to the size of transactions. At t…
SEC Form 13F-HR: Statistical investigation of trading imbalances and profitability analysis
Deborah Miori, Mihai Cucuringu
US Institutions with more than $100 million assets under management must disclose part of their long positions into the SEC Form 13F-HR on a quarterly basis. We consider the number…
Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes
Deborah Miori, Mihai Cucuringu
We define data-driven macroeconomic regimes by clustering the relative performance in time of indices belonging to different asset classes. We then investigate lead-lag relationshi…
Graph similarity learning for change-point detection in dynamic networks
Deborah Sulem, Henry Kenlay, Mihai Cucuringu +1
Dynamic networks are ubiquitous for modelling sequential graph-structured data, e.g., brain connectome, population flows and messages exchanges. In this work, we consider dynamic n…
Tail-GAN: Learning to Simulate Tail Risk Scenarios
Rama Cont, Mihai Cucuringu, Renyuan Xu +1
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components. We propose a novel data-…
Volatility forecasting with machine learning and intraday commonality
Chao Zhang, Yihuang Zhang, Mihai Cucuringu +1
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporati…
Option Volume Imbalance as a predictor for equity market returns
Nikolas Michael, Mihai Cucuringu, Sam Howison
We investigate the use of the normalized imbalance between option volumes corresponding to positive and negative market views, as a predictor for directional price movements in the…
Local2Global: A distributed approach for scaling representation learning on graphs
Lucas G. S. Jeub, Giovanni Colavizza, Xiaowen Dong +2
We propose a decentralised "local2global"' approach to graph representation learning, that one can a-priori use to scale any embedding technique. Our local2global approach proceeds…