arXiv research

Algorithm classifies market regimes using time series signatures.

Study improves S&P 500 volatility forecasting through regime-switching methods.

Clusters asset classes to identify lead-lag relationships in market regimes.

sWk-means clusters multidimensional financial time series into distinct market regimes.

New method detects and clusters market regimes in multidimensional data.

This paper introduces a novel model-based clustering approach for clustering time series which present changes in regime. It consists of a mixture of polynomial regressions governed by hidden Markov chains. The underlying hidden process for each cluster activates successively several polynomial regimes during time. The…

New method clusters financial time series into volatility regimes.

New model prices crypto options by clustering market regimes and using implied volatility.

The article detects market regimes from covariance matrices using VLSTAR and clustering models.

We consider two closely related problems: planted clustering and submatrix localization. The planted clustering problem assumes that a random graph is generated based on some underlying clusters of the nodes; the task is to recover these clusters given the graph. The submatrix localization problem concerns locating hid…

Paper proposes a new algorithm for clustering financial market regimes.

A hybrid approach detects financial market regime switches using PCA and k-means.

New lower bounds show challenges in clustering in moderate dimensions.

The paper proposes a method to cluster data and estimate regression parameters using VI for financial forecasting.

We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an underlying, yet hidden, geometric structure of the data. The pro…

New model clusters mixed-type data with missing values, improving air quality analysis.

Mixture model-based clustering, usually applied to multidimensional data, has become a popular approach in many data analysis problems, both for its good statistical properties and for the simplicity of implementation of the Expectation-Maximization (EM) algorithm. Within the context of a railway application, this pape…

For a certain class of distributions, we prove that the linear programming relaxation of $k$-medoids clustering---a variant of $k$-means clustering where means are replaced by exemplars from within the dataset---distinguishes points drawn from nonoverlapping balls with high probability once the number of points drawn a…

A new graph-based clustering method for moderate-dimensional data.

Quantum GBS boosts asset clustering for robust statistical arbitrage portfolios.

The paper analyzes $k$-means clustering for missing data, proving statistical guarantees under MCAR.

DIVI clusters noisy high-dimensional data with stable feature gating.

Regularized spectral methods improve clustering in signed graphs, especially for sparse data.

In this paper, we investigate community detection in networks in the presence of node covariates. In many instances, covariates and networks individually only give a partial view of the cluster structure. One needs to jointly infer the full cluster structure by considering both. In statistics, an emerging body of work …

The paper analyzes how clustering sensitive data can improve model generalization without revealing individual information.

Leveraged ETFs can outperform their targets in certain market conditions, contrary to the volatility drag hypothesis.

New algorithm achieves optimal clustering for sparse centers with high dimensions.

This paper introduces a novel mixture model-based approach for simultaneous clustering and optimal segmentation of functional data which are curves presenting regime changes. The proposed model consists in a finite mixture of piecewise polynomial regression models. Each piecewise polynomial regression model is associat…

This paper studies the optimality of kernel methods in high-dimensional data clustering. Recent works have studied the large sample performance of kernel clustering in the high-dimensional regime, where Euclidean distance becomes less informative. However, it is unknown whether popular methods, such as kernel k-means, …

Model predicts global financial market risks and asset allocation.

The subject of the present article is the study of correlations between large insurance companies and their contribution to systemic risk in the insurance sector. Our main goal is to analyze the conditional structure of the correlation on the European insurance market and to compare systemic risk in different regimes o…

Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT)…

New algorithm for clustering with faulty oracle achieves optimal queries and efficiency.

Study clusters distributions with known or unknown clusters using distribution testing.

Community detection is a fundamental unsupervised learning problem for unlabeled networks which has a broad range of applications. Many community detection algorithms assume that the number of clusters $r$ is known apriori. In this paper, we propose an approach based on semi-definite relaxations, which does not require…

Quantitative model predicts Sri Lankan stock market using NLP, clustering, and time-series forecasting.

The paper quantizes concatenated noisy vectors to a common cluster center, improving performance over naive methods.

The binary symmetric stochastic block model deals with a random graph of $n$ vertices partitioned into two equal-sized clusters, such that each pair of vertices is connected independently with probability $p$ within clusters and $q$ across clusters. In the asymptotic regime of $p=a \log n/n$ and $q=b \log n/n$ for fixe…

The paper analyzes the dynamics of tokens in transformer models at moderate interaction levels.

This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a re…

New spectral clustering method for multi-layer networks improves accuracy.

New algorithm improves hypergraph clustering for unbalanced communities.

We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning. Our infinite mixture prototypes represent each class by a set of clusters, unlike existing prototypical methods that represent each class by a single cluster. By inferring the number of clust…

This paper examines cryptocurrency integration with traditional markets, showing how network structure and turbulence influence cross-asset spillovers.

Hybrid model improves synthetic equity data generation.

Spectral clustering performance depends on eigenvector fluctuations, shown to be Gaussian.

Bayesian method clusters time series with varying dynamics.

Spectral clustering is a technique that clusters elements using the top few eigenvectors of their (possibly normalized) similarity matrix. The quality of spectral clustering is closely tied to the convergence properties of these principal eigenvectors. This rate of convergence has been shown to be identical for both th…