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.
The paper tackles fair correlation clustering with new algorithms and analysis.
Biological neurons learn tensor decompositions of higher-order correlations using nonlinear Hebbian plasticity.
New formulas for barrier options in stochastic volatility models with nonzero correlation.
In this paper, we develop a theory of common decomposition for two correlated Brownian motions, in which, by using change of time method, the correlated Brownian motions are represented by a triplet of processes, , where and are independent Brownian motions. We show the equivalent conditions for the tr…
We investigate aspects of semimartingale decompositions, approximation and the martingale representation for multidimensional correlated Markov processes. A new interpretation of the dependence among processes is given using the martingale approach. We show that it is possible to represent, in both continuous and discr…
Portfolio allocation and risk management make use of correlation matrices and heavily rely on the choice of a proper correlation matrix to be used. In this regard, one important question is related to the choice of the proper sample period to be used to estimate a stable correlation matrix. This paper addresses this qu…
PFDL improves deep learning models' OOD generalization by decorrelating feature embeddings.
Paper breaks down risk contribution into inherent and correlation risk components.
The paper proposes new cross-correlators using Price's Theorem and piecewise-linear decomposition.
The Empirical Mode Decomposition (EMD) provides a tool to characterize time series in terms of its implicit components oscillating at different time-scales. We apply this decomposition to intraday time series of the following three financial indices: the S\&P 500 (USA), the IPC (Mexico) and the VIX (volatility index US…
Two methods are proposed to filter correlations in DCC-GARCH residuals for foreign exchange rates.
The study analyzes XRP transaction networks to understand market dynamics.
New method uses conformal prediction for time series forecasting, accounting for temporal correlation.
A new method for traffic data imputation considering spatiotemporal correlations.
TGCCA analyzes higher-order tensors using orthogonal rank-R CP decomposition.
IKD uses eigen-decomposition for nonlinear dimensionality reduction.
PROD method improves high-dimensional regression by handling strong correlations.
Develops methods to analyze feature-outcome associations in subpopulations.
Canonical Correlation Analysis (CCA) is a widely used statistical tool with both well established theory and favorable performance for a wide range of machine learning problems. However, computing CCA for huge datasets can be very slow since it involves implementing QR decomposition or singular value decomposition of h…
The correlation coefficient between stocks depends on price history and includes information on hierarchical structure in financial markets. It is useful for portfolio selection and estimation of risk. I introduce the Life Time of Correlation between stocks prices to know how far we should investigate the price history…
Enhances sensitivity analysis for correlated inputs.
Derives adjoint formulas for matrix operations and applies them to specific cases.
Proposes BHT-ARIMA for forecasting multiple short time series.
GNCL algorithm controls diversity in deep ensembles.
DTCCA learns nonlinear transformations of multi-view data for high-order correlation.
D-GCCA improves multi-view data analysis by separating common and distinctive components.
We study Principal Component Analysis (PCA) in a setting where a part of the corrupting noise is data-dependent and, as a result, the noise and the true data are correlated. Under a bounded-ness assumption on the true data and the noise, and a simple assumption on data-noise correlation, we obtain a nearly optimal samp…
Simple model finds high correlation in retail crypto returns.
We review the decomposition method of stock return cross-correlations, presented previously for studying the dependence of the correlation coefficient on the resolution of data (Epps effect). Through a toy model of random walk/Brownian motion and memoryless renewal process (i.e. Poisson point process) of observation ti…
Tensor decompositions are powerful tools for large data analytics as they jointly model multiple aspects of data into one framework and enable the discovery of the latent structures and higher-order correlations within the data. One of the most widely studied and used decompositions, especially in data mining and machi…
Develops tools to decompose spurious variations in causal models.
The study of hierarchy in networks of the human brain has been of significant interest among the researchers as numerous studies have pointed out towards a functional hierarchical organization of the human brain. This paper provides a novel method for the extraction of hierarchical connectivity components in the human …
In multi-label learning, each sample is associated with several labels. Existing works indicate that exploring correlations between labels improve the prediction performance. However, embedding the label correlations into the training process significantly increases the problem size. Moreover, the mapping of the label …
We propose a Standing Wave Decomposition (SWD) approximation to Gaussian Process regression (GP). GP involves a costly matrix inversion operation, which limits applicability to large data analysis. For an input space that can be approximated by a grid and when correlations among data are short-ranged, the kernel matrix…
Combining neural networks and multiscale decomposition for financial market analysis.
DEMOTE uses neural diffusion-reaction processes to capture temporal dynamics in sparse tensor data.
The cohomology theory for financial market can allow us to deform Kolmogorov space of time series data over time period with the explicit definition of eight market states in grand unified theory. The anti-de Sitter space induced from a coupling behavior field among traders in case of a financial market crash acts like…
Word embedding is a powerful tool in natural language processing. In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase. We give a generative model that can capture specific syntactic relations between words. Under our …
Canonical Correlation Analysis (CCA) is a widely used spectral technique for finding correlation structures in multi-view datasets. In this paper, we tackle the problem of large scale CCA, where classical algorithms, usually requiring computing the product of two huge matrices and huge matrix decomposition, are computa…
New method uncovers hidden groups in CDS market not tied to standard industry classifications.
Finite mixtures of regression models offer a flexible framework for investigating heterogeneity in data with functional dependencies. These models can be conveniently used for unsupervised learning on data with clear regression relationships. We extend such models by imposing an eigen-decomposition on the multivariate …
In this paper we consider general rank minimization problems with rank appearing in either objective function or constraint. We first establish that a class of special rank minimization problems has closed-form solutions. Using this result, we then propose penalty decomposition methods for general rank minimization pro…
Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directions that contain most of its variability. Provably accurate solutions for PCA have been in use for a long time. However, to the best of our knowledge, all existing theoretical guarantees for it assume that …
We consider a decomposition method for compressive streaming data in the context of online compressive Robust Principle Component Analysis (RPCA). The proposed decomposition solves an - cluster-weighted minimization to decompose a sequence of frames (or vectors), into sparse and low-rank components, from com…
Study the structure of international trade through hypergraphs.
The paper identifies universal features for high-dimensional data inference.
Paper proposes a new LSTM model for spatio-temporal learning.
Financial markets, being spectacular examples of complex systems, display rich correlation structures among price returns of different assets. The correlation structures change drastically, akin to phase transitions in physical phenomena, as do the influential stocks (leaders) and sectors (communities), during market e…