Search Results for author:
Found 50 papers, 16 papers with code
Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering
This paper studies the directed graph clustering problem through the lens of statistics, where we formulate clustering as estimating underlying communities in the directed stochastic block model (DSBM).
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
For analysing real-world networks, graph representation learning is a popular tool.
Fin-GAN: forecasting and classifying financial time series via generative adversarial networks
We investigate the use of Generative Adversarial Networks (GANs) for probabilistic forecasting of financial time series.
Generative Adversarial Network
Probabilistic Time Series Forecasting
+2
Robust Angular Synchronization via Directed Graph Neural Networks
The angular synchronization problem aims to accurately estimate (up to a constant additive phase) a set of unknown angles $\theta_1, \dots, \theta_n\in[0, 2\pi)$ from $m$ noisy measurements of their offsets $\theta_i-\theta_j \;\mbox{mod} \; 2\pi.$ Applications include, for example, sensor network localization, phase retrieval, and distributed clock synchronization.
Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models
In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other.
Graph Neural Networks for Forecasting Multivariate Realized Volatility with Spillover Effects
We present a novel methodology for modeling and forecasting multivariate realized volatilities using customized graph neural networks to incorporate spillover effects across stocks.
SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation
SaGess then constructs a synthetic graph using the subgraphs that have been generated by DiGress.
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models
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 shifted in time relative to one another, and which can be leveraged for the purposes of control, forecasting or clustering.
OFTER: An Online Pipeline for Time Series Forecasting
We introduce OFTER, a time series forecasting pipeline tailored for mid-sized multivariate time series.
Co-trading networks for modeling dynamic interdependency structures and estimating high-dimensional covariances in US equity markets
The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States.
DeFi: data-driven characterisation of Uniswap v3 ecosystem & an ideal crypto law for liquidity pools
We conclude our work by testing for relationships between the characteristic mechanisms of each pool, i. e. liquidity provision, consumption, and price variation.
Symphony in the Latent Space: Provably Integrating High-dimensional Techniques with Non-linear Machine Learning Models
This paper revisits building machine learning algorithms that involve interactions between entities, such as those between financial assets in an actively managed portfolio, or interactions between users in a social network.
Trade Co-occurrence, Trade Flow Decomposition, and Conditional Order Imbalance in Equity Markets
The time proximity of high-frequency trades can contain a salient signal.
SEC Form 13F-HR: Statistical investigation of trading imbalances and profitability analysis
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.
Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes
We define data-driven macroeconomic regimes by clustering the relative performance in time of indices belonging to different asset classes.
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
In these experiments, we consider tasks related to signed information, tasks related to directional information, and tasks related to both signed and directional information.
Graph similarity learning for change-point detection in dynamic networks
The main novelty of our method is to use a siamese graph neural network architecture for learning a data-driven graph similarity function, which allows to effectively compare the current graph and its recent history.
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Generative models for network time series (also known as dynamic graphs) have tremendous potential in fields such as epidemiology, biology and economics, where complex graph-based dynamics are core objects of study.
Tail-GAN: Learning to Simulate Tail Risk Scenarios
The estimation of loss distributions for dynamic portfolios requires the simulation of scenarios representing realistic joint dynamics of their components, with particular importance devoted to the simulation of tail risk scenarios.
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs
While many networks are signed or directed, or both, there is a lack of unified software packages on graph neural networks (GNNs) specially designed for signed and directed networks.
Volatility forecasting with machine learning and intraday commonality
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility.
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
In this paper, we introduce neural networks into the ranking recovery problem by proposing the so-called GNNRank, a trainable GNN-based framework with digraph embedding.
Option Volume Imbalance as a predictor for equity market returns
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 spot market.
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market
In this paper, we propose a method for the detection of lead-lag clusters of time series in multivariate systems.
Local2Global: A distributed approach for scaling representation learning on graphs
Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.
Cross-Impact of Order Flow Imbalance in Equity Markets
We investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting.
A Universal End-to-End Approach to Portfolio Optimization via Deep Learning
The designed framework circumvents the traditional forecasting step and avoids the estimation of the covariance matrix, lifting the bottleneck for generalizing to a large amount of instruments.
SSSNET: Semi-Supervised Signed Network Clustering
Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited.
Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets
In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trades data from the most liquid Bitcoin markets, in order to understand the price formation process at sub-1 second time scales.
Local2Global: Scaling global representation learning on graphs via local training
Our local2global approach proceeds by first dividing the input graph into overlapping subgraphs (or "patches") and training local representations for each patch independently.
DUKweb: Diachronic word representations from the UK Web Archive corpus
Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications.
DIGRAC: Digraph Clustering Based on Flow Imbalance
DIGRAC optimizes directed flow imbalance for clustering without requiring label supervision, unlike existing graph neural network methods, and can naturally incorporate node features, unlike existing spectral methods.
An extension of the angular synchronization problem to the heterogeneous setting
This can be thought of as a natural extension of the angular synchronization problem to the heterogeneous setting of multiple groups of angles, where the measurement graph has an unknown edge-disjoint decomposition $G = G_1 \cup G_2 \ldots \cup G_k$, where the $G_i$'s denote the subgraphs of edges corresponding to each group.
Regularized spectral methods for clustering signed networks
We study the problem of $k$-way clustering in signed graphs.
A Linear Transportation $\mathrm{L}^p$ Distance for Pattern Recognition
The transportation $\mathrm{L}^p$ distance, denoted $\mathrm{TL}^p$, has been proposed as a generalisation of Wasserstein $\mathrm{W}^p$ distances motivated by the property that it can be applied directly to colour or multi-channelled images, as well as multivariate time-series without normalisation or mass constraints.
Spectral Ranking with Covariates
We consider spectral approaches to the problem of ranking n players given their incomplete and noisy pairwise comparisons, but revisit this classical problem in light of player covariate information.
Motif-Based Spectral Clustering of Weighted Directed Networks
We conclude that motif-based spectral clustering is a valuable tool for analysis of directed and bipartite weighted networks, which is also scalable and easy to implement.
Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing
Pricing assets has attracted significant attention from the financial technology community.
Mining the UK Web Archive for Semantic Change Detection
Semantic change detection (i. e., identifying words whose meaning has changed over time) started emerging as a growing area of research over the past decade, with important downstream applications in natural language processing, historical linguistics and computational social science.
Hermitian matrices for clustering directed graphs: insights and applications
Graph clustering is a basic technique in machine learning, and has widespread applications in different domains.
Ranking and synchronization from pairwise measurements via SVD
Given a measurement graph $G= (V, E)$ and an unknown signal $r \in \mathbb{R}^n$, we investigate algorithms for recovering $r$ from pairwise measurements of the form $r_i - r_j$; $\{i, j\} \in E$.
SPONGE: A generalized eigenproblem for clustering signed networks
We introduce a principled and theoretically sound spectral method for $k$-way clustering in signed graphs, where the affinity measure between nodes takes either positive or negative values.
An MBO scheme for clustering and semi-supervised clustering of signed networks
We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative.
Anomaly Detection in Networks with Application to Financial Transaction Networks
The first set of synthetic networks was split in a training set of 70 percent of the networks, and a test set of 30 percent of the networks.
Applications Social and Information Networks Physics and Society 05C82
Modeling outcomes of soccer matches
The parameters of the Bradley-Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalized version of it, while the posterior densities of the parameters of the hierarchical Poisson log-linear model are approximated using integrated nested Laplace approximations.
Provably robust estimation of modulo 1 samples of a smooth function with applications to phase unwrapping
Given the samples $(x_i, y_i)_{i=1}^{n}$, our goal is to recover smooth, robust estimates of the clean samples $f(x_i) \bmod 1$.
On denoising modulo 1 samples of a function
Given the samples $(x_i, y_i)_{i=1}^{n}$ our goal is to recover smooth, robust estimates of the clean samples $f(x_i) \bmod 1$.
Rank Aggregation for Course Sequence Discovery
In this work, we adapt the rank aggregation framework for the discovery of optimal course sequences at the university level.
Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and Semidefinite Programming Synchronization
We propose a similar synchronization-based algorithm for the rank-aggregation problem, which integrates in a globally consistent ranking pairwise comparisons given by different rating systems on the same set of items.
Point Localization and Density Estimation from Ordinal kNN graphs using Synchronization
We demonstrate the scalability of our approach on large graphs, and show how it compares to the Local Ordinal Embedding (LOE) algorithm, which was recently proposed for recovering the configuration of a cloud of points from pairwise ordinal comparisons between a sparse set of distances.
