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.
Study shows conventional data prep fails for insurance data, proposing new methods.
An introduction to the theory of bundle gerbes and their relationship to Hitchin-Chatterjee gerbes is presented. Topics covered are connective structures, triviality and stable isomorphism as well as examples and applications.
The purpose of this paper is to synthesize the approaches taken by Chatterjee-Meckes and Reinert-Röllin in adapting Stein's method of exchangeable pairs for multivariate normal approximation. The more general linear regression condition of Reinert-Röllin allows for wider applicability of the method, while the method of…
A dangerously brief history of the developments of the main ideas in economics, as observed by a physicist, is given. This was published in 'Econophysics of Stock and Other Markets', Eds. A. Chatterjee, B. K. Chakrabarti, New Economic Windows Series, Springer, Milan, 2006, pp~219-224.
We study the model of interacting agents proposed by Chatterjee et al that allows agents to both save and exchange wealth. Closed equations for the wealth distribution are developed using a mean field approximation. We show that when all agents have the same fixed savings propensity, subject to certain well defined app…
Paper shows ERM's suboptimality due to bias, not variance.
Continuous-time SGD converges under certain conditions, useful for deep learning.
New computations for third and fourth cohomology of Lie group spaces.
We study a mean-field version of rank-based models of equity markets such as the Atlas model introduced by Fernholz in the framework of Stochastic Portfolio Theory. We obtain an asymptotic description of the market when the number of companies grows to infinity. Then, we discuss the long-term capital distribution. We r…
SGD converges with positive probability for non-convex deep neural networks under specific conditions.
Study high-dimensional Bayesian linear regression using variational inference.
We construct a gerbe over a complex reductive Lie group G attached to an invariant bilinear form on a maximal diagonalizable subalgebra which is Weyl group invariant and satisfies a parity condition. By restriction to a maximal compact subgroup K, one then gets a gerbe over K. For a simply-connected group, the parity c…
The paper develops a cross-validation method for improving signal denoising techniques.
The paper develops concentration inequalities for structured random data, extending beyond independent terms.
Various multi-agent models of wealth distributions defined by microscopic laws regulating the trades, with or without a saving criterion, are reviewed. We discuss and clarify the equilibrium properties of the model with constant global saving propensity, resulting in Gamma distributions, and their equivalence to the Ma…
Estimates inverse temperature of Ising models with a single sample.
New metric -coherence measures gradient alignment during training, revealing surprising memorization patterns.
This work optimizes induced correlation in joint graph embeddings.
In this paper we use wavelet concepts to show that correlation coefficient between two financial data's is not constant but varies with scale from high correlation value to strongly anti-correlation value This studies is important because correlation coefficient is used to quantify degree of independence between two va…
We analyze the daily stock data of the Nasdaq Composite index in the 22-year period 1992-2013 and identify market states as clusters of correlation matrices with similar correlation structures. We investigate the stability of the correlation structure of each state by estimating the statistical fluctuations of correlat…
The study uses DCC for financial market analysis, revealing hidden correlations.
This study uses local Gaussian correlation to analyze stock return tails, revealing more sensitive network properties.
Polynomial time algorithm matches correlated Gaussian matrices without vanishing correlation.
This paper treats the problem of screening for variables with high correlations in high dimensional data in which there can be many fewer samples than variables. We focus on threshold-based correlation screening methods for three related applications: screening for variables with large correlations within a single trea…
This paper introduces anti-correlation networks to study China's stock market.
The study shows how trade uncertainty affects stock-bond correlations over time.
Infinite CNNs lose spatial correlations, but can be restored by correlated weights.
We discuss some methods to quantitatively investigate the properties of correlation matrices. Correlation matrices play an important role in portfolio optimization and in several other quantitative descriptions of asset price dynamics in financial markets. Specifically, we discuss how to define and obtain hierarchical …
This research examines rare spurious correlations in neural networks and their impact on accuracy and privacy.
The study reveals how synaptic correlations promote dimension reduction in neural networks.
Proposes PSCCA for estimating correlations and canonical correlations in sparse count data.
Study examines NFT market dynamics using correlation and noise analysis.
Polynomial-time algorithm matches correlated random graphs with non-vanishing correlation.
Proposes a multi-view VAE for imputing missing data from correlated sources.
Neurons in the visual cortex are correlated in their variability. The presence of correlation impacts cortical processing because noise cannot be averaged out over many neurons. In an effort to understand the functional purpose of correlated variability, we implement and evaluate correlated noise models in deep convolu…
Enhances community detection in correlated networks with node attributes.
New method detects intrinsic cross-correlations in non-stationary time series affected by common factors.
Develops correlation number for specific potentials and Hitchin representations.
The study finds significant power-law cross correlations in Bitcoin's return-volatility dynamics.
Financial markets analyzed by reducing correlation matrix complexity.
We study power-law correlations properties of the Google search queries for Dow Jones Industrial Average (DJIA) component stocks. Examining the daily data of the searched terms with a combination of the rescaled range and rescaled variance tests together with the detrended fluctuation analysis, we show that the searche…
Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest. While existing correlation measures are suitable for discovering average correlation, they fail to discover hidden or potential correlations. To bridge this gap, (i) we postulate a set of natural axioms …
This letter explores the behavior of conditional correlations among main cryptocurrencies, stock and bond indices, and gold, using a generalized DCC class model. From a portfolio management point of view, asset correlation is a key metric in order to construct efficient portfolios. We find that: (i) correlations among …
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…
Improved portfolio optimization using Kendall-like correlation coefficients.
We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform corre…
In 2012, JPMorgan accumulated a USD~6.2 billion loss on a credit derivatives portfolio, the so-called `London Whale', partly as a consequence of de-correlations of non-perfectly correlated positions that were supposed to hedge each other. Motivated by this case, we devise a factor model for correlations that allows for…
New study shows FTRL mechanism works with correlated events.