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.
A parsimonious model reduces over-parameterization in skewed matrix variate mixtures.
A mixture of common skew-t factor analyzers model is introduced for model-based clustering of high-dimensional data. By assuming common component factor loadings, this model allows clustering to be performed in the presence of a large number of mixture components or when the number of dimensions is too large to be well…
MSFA clusters high-dimensional spatial data using spline-based covariance structures.
A mixture of factor analyzers is a semi-parametric density estimator that generalizes the well-known mixtures of Gaussians model by allowing each Gaussian in the mixture to be represented in a different lower-dimensional manifold. This paper presents a robust and parsimonious model selection algorithm for training a mi…
The paper learns pose variations within shape populations using constrained mixtures of factor analyzers.
A hybrid method clusters and characterizes cancer data efficiently.
Flexible models cluster RNA sequencing data.
Model-based clustering imposes a finite mixture modelling structure on data for clustering. Finite mixture models assume that the population is a convex combination of a finite number of densities, the distribution within each population is a basic assumption of each particular model. Among all distributions that have …
We study the stability vis a vis adversarial noise of matrix factorization algorithm for matrix completion. In particular, our results include: (I) we bound the gap between the solution matrix of the factorization method and the ground truth in terms of root mean square error; (II) we treat the matrix factorization as …
This paper analyzes privacy threats in federated matrix factorization.
Develops a hybrid MtFA approach for high-dimensional data clustering.
The paper analyzes market risk factors for a mining company using a VAR model with stable distribution.
Paper analyzes factors affecting COVID-19 risk in US counties.
Regression Trees analyze stock returns, revealing market excess return as the most informative factor.
The paper formalizes and analyzes multi-agent Q-learning with value factorization.
In a very high-dimensional vector space, two randomly-chosen vectors are almost orthogonal with high probability. Starting from this observation, we develop a statistical factor model, the random factor model, in which factors are chosen at random based on the random projection method. Randomness of factors has the con…
The paper analyzes frameworks for integrating sustainability into investment decisions.
Study analyzes correlation structure in two-factor Hull-White model for XVA calculations.
Paper predicts international trade flows using machine learning and factorization models.
Over the years data has become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for clustering (unsupervised classification) as well as semi-supervised and supervised classification. Although dimension reduction in the area of clus…
We analyze linear factor models for asset pricing panels.
Mixed membership factorization is a popular approach for analyzing data sets that have within-sample heterogeneity. In recent years, several algorithms have been developed for mixed membership matrix factorization, but they only guarantee estimates from a local optimum. Here, we derive a global optimization (GOP) algor…
This paper compares two stock factor models in China's A-share market.
An algorithm was recently introduced by INTECH for the purposes of estimating the trading-profit contribution of systematic rebalancing to the relative return of rules-based investment strategies. We apply this methodology to analyze the size factor through the use of equal-weighted portfolios. These strategies combine…
Study analyzes bond price covariation robustly under no-arbitrage conditions.
Extracts factors from Treasury yields using ML techniques.
We present a Bayesian non-negative tensor factorization model for count-valued tensor data, and develop scalable inference algorithms (both batch and online) for dealing with massive tensors. Our generative model can handle overdispersed counts as well as infer the rank of the decomposition. Moreover, leveraging a repa…
This paper considers a restriction to non-negative matrix factorization in which at least one matrix factor is stochastic. That is, the elements of the matrix factors are non-negative and the columns of one matrix factor sum to 1. This restriction includes topic models, a popular method for analyzing unstructured data.…
In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such techniques are firmly established in the literature for multivariate data, there is a relative paucity in the area of matrix variate, or three-w…
We study valuation of swing options on commodity markets when the commodity prices are driven by multiple factors. The factors are modeled as diffusion processes driven by a multidimensional Lévy process. We set up a valuation model in terms of a dynamic programming problem where the option can be exercised continuousl…
New statistical factors improve portfolio risk estimation.
Model-based collaborative filtering analyzes user-item interactions to infer latent factors that represent user preferences and item characteristics in order to predict future interactions. Most collaborative filtering algorithms assume that these latent factors are static, although it has been shown that user preferen…
We study fillings of contact structures supported by planar open books by analyzing positive factorizations of their monodromy. Our method is based on Wendl's theorem on symplectic fillings of planar open books. We prove that every virtually overtwisted contact structure on L(p,1) has a unique filling, and describe fil…
A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.
This study introduces a new GAS blending ensemble model for Bitcoin price prediction.
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data. It can be viewed as an extension of Poisson matrix factorization (PF) perturbed by a multiplicative term which models exposure. This term brings a degree of freedom fo…
New model analyzes dynamic correlations in stock returns.
Study analyzes factors influencing healthcare providers' engagement with SMS campaigns.
This paper proposes a parametric approach for stochastic modeling of limit order markets. The models are obtained by augmenting classical perfectly liquid market models by few additional risk factors that describe liquidity properties of the order book. The resulting models are easy to calibrate and to analyze using st…
Deep weight factorization improves neural network training through smooth optimization of sparse penalties.
NCFA uses deep learning and causal discovery to analyze complex data.
Binary data matrices can represent many types of data such as social networks, votes, or gene expression. In some cases, the analysis of binary matrices can be tackled with nonnegative matrix factorization (NMF), where the observed data matrix is approximated by the product of two smaller nonnegative matrices. In this …
Proposes MLDP for modeling multilinear data.
The paper analyzes DeepWalk and node2vec for community detection in stochastic blockmodels.
Study analyzes optimal execution under uncertain volatility and liquidity.
Factor analysis is a statistical technique employed to evaluate how observed variables correlate through common factors and unique variables. While it is often used to analyze price movement in the unstable stock market, it does not always yield easily interpretable results. In this study, we develop improved factor mo…
This study analyzes prediction risk for PCR method in latent factor regression models.
Proposes CC-NMDF for analyzing manifold-valued data.