A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Proposes a hierarchical clustering method for positive and negative dissimilarities.
Clusters of highly correlated stocks are identified for better asset selection.
CCP clusters correlated features and projects them to 1D for efficient dimensionality reduction.
CSTS benchmarks time series clustering by evaluating correlation structures.
A new algorithm removes unexpected correlations in biased data for better clustering.
Active learning optimizes correlation clustering by querying the most informative pairwise comparisons.
DynMSA detects market clusters for better portfolio allocation.
In this paper, we study correlation clustering under fairness constraints. Fair variants of -median and -center clustering have been studied recently, and approximation algorithms using a notion called fairlet decomposition have been proposed. We obtain approximation algorithms for fair correlation clustering und…
This paper analyzes correlations in patterns of trading of different members of the London Stock Exchange. The collection of strategies associated with a member institution is defined by the sequence of signs of net volume traded by that institution in hour intervals. Using several methods we show that there are signif…
Paper uses HPCA for better stock correlation modeling.
Clusters of financial market states identified over 2006-2019.
The study uses DCC for financial market analysis, revealing hidden correlations.
New RDPC dissimilarity measure improves time series clustering.
Paper develops active learning for clustering unknown pairwise similarities.
In this paper we study the problem of correlation clustering under fairness constraints. In the classic correlation clustering problem, we are given a complete graph where each edge is labeled positive or negative. The goal is to obtain a clustering of the vertices that minimizes disagreements -- the number of negative…
This paper optimizes cryptocurrency portfolios by clustering price correlations and improving risk-return profiles.
Cluster GARCH model improves multivariate GARCH for high-dimensional asset returns.
A measure called relative cluster entropy distinguishes between correlated and uncorrelated sequences.
We describe a new optimization scheme for finding high-quality correlation clusterings in planar graphs that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on the energy of the optimal correlation clustering that are typically fast to compute and tight in practice. We demonstrate our a…
Quantum GBS boosts asset clustering for robust statistical arbitrage portfolios.
In correlation clustering, we are given objects together with a binary similarity score between each pair of them. The goal is to partition the objects into clusters so to minimise the disagreements with the scores. In this work we investigate correlation clustering as an active learning problem: each similarity sc…
Cluster stability selection improves feature selection in correlated data.
This paper studies ordered weighted L1 (OWL) norm regularization for sparse estimation problems with strongly correlated variables. We prove sufficient conditions for clustering based on the correlation/colinearity of variables using the OWL norm, of which the so-called OSCAR is a particular case. Our results extend pr…
Data de-duplication is the task of detecting multiple records that correspond to the same real-world entity in a database. In this work, we view de-duplication as a clustering problem where the goal is to put records corresponding to the same physical entity in the same cluster and putting records corresponding to diff…
This article investigates the correlation structure of the global crude oil market using the daily returns of 71 oil price time series across the world from 1992 to 2012. We identify from the correlation matrix six clusters of time series exhibiting evident geographical traits, which supports Weiner's (1991) regionaliz…
End-to-end deep learning for multi-view clustering improves accuracy across various data types.
Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, KwikCluster in practice re…
VC-PCR improves prediction by clustering correlated variables.
Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our mai…
Spatially relaxed inference tackles high-dimensional linear models with correlated covariates.
In this work, the possibility of clustering correlated random variables was examined, both because of their mutual similarity and because of their similarity to the principal components. The k-means algorithm and spectral algorithms were used for clustering. For spectral methods, the similarity matrix was both the matr…
New method clusters evolving networks using spatio-temporal graph Laplacian.
FASC clusters data with latent factors, improving on naive methods.
This study uses moving average cluster entropy to analyze financial market dynamics.
The cluster analysis methods are used in order to perform a comparative study of 15 EU countries in relation with the fluctuations of some basic macroeconomic indicators. The statistical distances between countries are calculated for various moving time windows, and the time variation of the mean statistical distance i…
In this paper, we introduce Adaptive Cluster Lasso(ACL) method for variable selection in high dimensional sparse regression models with strongly correlated variables. To handle correlated variables, the concept of clustering or grouping variables and then pursuing model fitting is widely accepted. When the dimension is…
We study the structure of locational marginal prices in day-ahead and real-time wholesale electricity markets. In particular, we consider the case of two North American markets and show that the price correlations contain information on the locational structure of the grid. We study various clustering methods and intro…
Researchers have used from 30 days to several years of daily returns as source data for clustering financial time series based on their correlations. This paper sets up a statistical framework to study the validity of such practices. We first show that clustering correlated random variables from their observed values i…
Geometric QHD tests improve hub detection in correlated data.
Clusters cryptocurrency market states via cross correlation analysis.
Variable clustering is important for explanatory analysis. However, only few dedicated methods for variable clustering with the Gaussian graphical model have been proposed. Even more severe, small insignificant partial correlations due to noise can dramatically change the clustering result when evaluating for example w…
This paper presents a novel application of a clustering algorithm developed for constructing a phylogenetic network to the correlation matrix for 126 stocks listed on the Shanghai A Stock Market. We show that by visualizing the correlation matrix using a Neighbor-Net network and using the circular ordering produced dur…
A new method captures higher-order interactions in data clusters.
Develops a new random forest method for clustered data with improved prediction and inference.
We review the state of the art of clustering financial time series and the study of their correlations alongside other interaction networks. The aim of this review is to gather in one place the relevant material from different fields, e.g. machine learning, information geometry, econophysics, statistical physics, econo…
The paper explores how macroeconomic variables' correlation structure changes over time and under different scenarios.
Cluster jackknife improves inference for staggered DID methods.
New method separates market motion from stock correlations.