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 examines how global economic policy uncertainty affects crude oil futures volatility.
Framework identifies causal factors of climate change using correlations and machine learning.
We develop a monitoring procedure to detect changes in a large approximate factor model. Letting be the number of common factors, we base our statistics on the fact that the -th eigenvalue of the sample covariance matrix is bounded under the null of no change, whereas it becomes spiked under cha…
A justification of the Basel liquidity formula for risk capital in the trading book is given under the assumption that market risk-factor changes form a Gaussian white noise process over 10-day time steps and changes to P&L are linear in the risk-factor changes. A generalization of the formula is derived under the more…
We empirically analyze the price and liquidity responses to trade signs, traded volumes and signed traded volumes. Utilizing the singular value decomposition, we explore the interconnections of price responses and of liquidity responses across the whole market. The statistical characteristics of their singular vectors …
Model for dynamic relational data with regime changes.
Audit fees change based on company and economic factors during auditor switching.
Framework LiLY recovers latent causal variables from time-series data under distribution shifts.
Intelligent agents should be able to learn useful representations by observing changes in their environment. We model such observations as pairs of non-i.i.d. images sharing at least one of the underlying factors of variation. First, we theoretically show that only knowing how many factors have changed, but not which o…
The study examines cross-border lending behavior from G7 countries, showing changes in driving factors after the 2008 financial crisis.
Hedonic models predict 84-92% of U.S. real estate prices, highlighting environmental factors' impact.
Study on special anisotropic conformal changes of conic pseudo-Finsler surfaces.
Proposes tPARAFAC2 for tracking evolving patterns in time-evolving data.
TTLSA adapts models to label shifts across domains with nuisance factors.
Quantization of neural networks has become common practice, driven by the need for efficient implementations of deep neural networks on embedded devices. In this paper, we exploit an oft-overlooked degree of freedom in most networks - for a given layer, individual output channels can be scaled by any factor provided th…
Style Miner generates stable and significant style factors for time series analysis.
Study models risks for low-carbon economy in Balkan countries, focusing on shadow economy and populism.
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that…
The common wisdom argues that, in general, large trades cause large price changes, while small trades cause small price changes. However, for extremely large price changes, the trade size and news play a minor role, while the liquidity (especially price gaps on the limit order book) is a more influencing factor. Hence,…
This work introduces a method to attribute model performance drops to distribution shifts.
We consider the problem of optimal portfolio selection under forward investment performance criteria in an incomplete market. The dynamics of the prices of the traded assets depend on a pair of stochastic factors, namely, a slow factor (e.g. a macroeconomic indicator) and a fast factor (e.g. stochastic volatility). We …
This paper uses PCA and FA for feature selection in credit rating.
It has been postulated that a good representation is one that disentangles the underlying explanatory factors of variation. However, it remains an open question what kind of training framework could potentially achieve that. Whereas most previous work focuses on the static setting (e.g., with images), we postulate that…
In electricity markets, it is sensible to use a two-factor model with mean reversion for spot prices. One of the factors is an Ornstein-Uhlenbeck (OU) process driven by a Brownian motion and accounts for the small variations. The other factor is an OU process driven by a pure jump Lévy process and models the characteri…
New method detects changes in high-dimensional data from small samples.
Alpha-R1 uses LLMs to reason about economic factors and news for better alpha screening.
A Bayesian Boolean Matrix Factorization for cancer genomics
Unified framework for disentangled representations using mechanistic independence.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
We introduce a class of randomly time-changed fast mean-reverting stochastic volatility models and, using spectral theory and singular perturbation techniques, we derive an approximation for the prices of European options in this setting. Three examples of random time-changes are provided and the implied volatility sur…
New method extracts brain age from MRI sequences over time.
Global oil price is an important factor in determining many economic variables in the world's economy. It is generally modeled as a stochastic process and have been studied through different techniques by comparing the historic time series of demand, supply and the price itself. However, there are many historic events …
Gradient descent proves global convergence for 4-layer matrix factorization.
Paper proves achiral Lefschetz fibrations from non-orientable Lefschetz fibrations.
Unified multilinear model for causal factor disentanglement.
New methods for -transform inversion and Wiener-Hopf factorization.
TRACE analyzes risk changes in models trained on shifted data.
We use self-report and electrodermal activity (EDA) wearable sensor data from 77 nights of sleep on six participants to test the efficacy of EDA data for sleep monitoring. We used factor analysis to find latent factors in the EDA data, and causal model search to find the most probable graphical model accounting for sel…
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…
In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such time-varying smoothness is not accounted for, one can obtain misleading inf…
Trading strategy uses Hoeffding's Inequality to predict financial regime change.
A new framework based on the theory of copulas is proposed to address semi- supervised domain adaptation problems. The presented method factorizes any multivariate density into a product of marginal distributions and bivariate cop- ula functions. Therefore, changes in each of these factors can be detected and corrected…
This paper analyses the Chinese Sovereign bond yield to find out the principal factors affecting the term structure of interest rate changes. We apply Principal Component Analysis (PCA) on our data consisting of the Chinese Sovereign bond from January 2002 till May 2018 with the different yield to maturity. Then we wil…
Paper presents adaptive minimax risk classifiers for multidimensional concept drift.
Study uses APT and QR to identify risk factors affecting crude oil returns.
A new jump diffusion regime-switching model is introduced, which allows for linking jumps in asset prices with regime changes. We prove the existence and uniqueness of the solution to the risk-sensitive asset management criterion maximisation problem in this setting. We provide an ODE for the optimal value function, wh…
CDSSD detects sparse changes in partially observable data streams.
In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a specific factor and assume that we know how the prior changes between source and target domains. We call this factor an attribute, and reformu…