10 citations · 29 across the 25 of their papers we have counts for
7 papers · 1 filter
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…