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 study shows how trade uncertainty affects stock-bond correlations over time.
Paper proposes a CNN model for improved multi-asset portfolio risk prediction.
Paper proposes a supervised similarity framework for corporate bonds using RF proximities.
In mutual fund, an investment adviser gives advice to clients about investing in securities such as stocks, bonds, mutual funds, or exchange traded funds. Some investment advisers manage portfolios of securities. In this paper, we analyze advisor portfolio for each advisor so as to recognize the pattern in each adviser…
Improved GAS models using trees and forests for better forecasts.
This paper identifies and analyzes biases in risk-adjusted index weighting methods, affecting social welfare and market fairness.
Modified CTGAN-Plus-Features method optimizes asset allocation with CVaR constraint.
Market timing is an investment technique that tries to continuously switch investment into assets forecast to have better returns. What is the likelihood of having a successful market timing strategy? With an emphasis on modeling simplicity, I calculate the feasible set of market timing portfolios using index mutual fu…
This paper examines momentum spillover across multiple asset classes using only pricing data.
Research develops a DSS for stock selection and asset allocation using fundamental data.
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.
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.
We propose a group model for correlations in stock markets. In the group model the markets are composed of several groups, within which the stock price fluctuations are correlated. The spectral properties of empirical correlation matrices reported in [Phys. Rev. Lett. {\bf 83}, 1467 (1999); Phys. Rev. Lett. {\bf 83}, 1…
This paper generalizes Moody's correlated binomial default distribution for homogeneous (exchangeable) credit portfolio, which is introduced by Witt, to the case of inhomogeneous portfolios. As inhomogeneous portfolios, we consider two cases. In the first case, we treat a portfolio whose assets have uniform default cor…
The paper shows how cross-ownership increases equity correlations during financial crises.
Financial correlation matrices measure the unsystematic correlations between stocks. Such information is important for risk management. The correlation matrices are known to be ``noise dressed''. We develop a new and alternative method to estimate this noise. To this end, we simulate certain time series and random matr…
This work discusses the problem of sparse signal recovery when there is correlation among the values of non-zero entries. We examine intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector model, as well as their combination. Algor…
We investigate how simultaneously recorded long-range power-law correlated multi-variate signals cross-correlate. To this end we introduce a two-component ARFIMA stochastic process and a two-component FIARCH process to generate coupled fractal signals with long-range power-law correlations which are at the same time lo…
Paper studies estimating asset correlations across sectors.
CaLoNet integrates spatial and local correlations for multivariate time series classification.