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 suggests using information flow measures to target interventions in neural networks.
This study analyzes information flow networks in Chinese stock sectors using transfer entropy.
In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much in…
We empirically investigated the effects of market factors on the information flow created from N(N-1)/2 linkage relationships among stocks. We also examined the possibility of employing the minimal spanning tree (MST) method, which is capable of reducing the number of links to N-1. We determined that market factors car…
Using transfer entropy, we observed the strength and direction of information flow between stock indices. We uncovered that the biggest source of information flow is America. In contrast, the Asia/Pacific region the biggest is receives the most information. According to the minimum spanning tree, the GSPC is located at…
We investigated financial market data to determine which factors affect information flow between stocks. Two factors, the time dependency and the degree of efficiency, were considered in the analysis of Korean, the Japanese, the Taiwanese, the Canadian, and US market data. We found that the frequency of the significant…
New method estimates mutual information using normalizing flows.
We introduce a framework for Newton's flows in probability space with information metrics, named information Newton's flows. Here two information metrics are considered, including both the Fisher-Rao metric and the Wasserstein-2 metric. A known fact is that overdamped Langevin dynamics correspond to Wasserstein gradien…
In this paper, we quantify the statistical coherence between financial time series by means of the Renyi entropy. With the help of Campbell's coding theorem we show that the Renyi entropy selectively emphasizes only certain sectors of the underlying empirical distribution while strongly suppressing others. This accentu…
In the information-based approach to asset pricing the market filtration is modelled explicitly as a superposition of signals concerning relevant market factors and independent noise. The rate at which the signal is revealed to the market then determines the overall magnitude of asset volatility. By letting this inform…
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
This paper presents an overview of information-based asset pricing. In this approach, an asset is defined by its cash-flow structure. The market is assumed to have access to "partial" information about future cash flows. Each cash flow is determined by a collection of independent market factors called X-factors. The ma…
CPFM integrates dimensionality reduction and reconstruction with flow networks.
The information-based asset-pricing framework of Brody, Hughston and Macrina (BHM) is extended to include a wider class of models for market information. In the BHM framework, each asset is associated with a collection of random cash flows. The price of the asset is the sum of the discounted conditional expectations of…
New MIF architecture improves posterior approximations in Bayesian models.
Measuring information value in markets using covariance of price changes and order flow.
The article reviews how gradient flow systems on hypergraphs connect to information geometry and nonequilibrium physics.
We model continuous-time information flows generated by a number of information sources that switch on and off at random times. By modulating a multi-dimensional Lévy random bridge over a random point field, our framework relates the discovery of relevant new information sources to jumps in conditional expectation mart…
A new framework for asset price dynamics is introduced in which the concept of noisy information about future cash flows is used to derive the price processes. In this framework an asset is defined by its cash-flow structure. Each cash flow is modelled by a random variable that can be expressed as a function of a colle…
In financial markets, the information that traders have about an asset is reflected in its price. The arrival of new information then leads to price changes. The `information-based framework' of Brody, Hughston and Macrina (BHM) isolates the emergence of information, and examines its role as a driver of price dynamics.…
A Smale flow is a structurally stable flow with one dimensional invariant sets. We use information from homology and template theory to construct, visualize and in some cases, classify, nonsingular Smale flows in the 3-sphere.
New method uses entropy dissipation to prove isoperimetric inequalities.
Study quantifies information flow in neural networks using relative entropy and RG analogy.
Investor flows in Korean equity market transmit shared information, not private signals.
An asymmetric information model is introduced for the situation in which there is a small agent who is more susceptible to the flow of information in the market than the general market participant, and who tries to implement strategies based on the additional information. In this model market participants have access t…
Neural estimator improves mutual information estimation in high dimensions.
Paper develops physics-informed, boundary-constrained Gaussian process for fluid flow field reconstruction.
Labor market institutions are central for modern economies, and their polices can directly affect unemployment rates and economic growth. At the individual level, unemployment often has a detrimental impact on people's well-being and health. At the national level, high employment is one of the central goals of any econ…
Matched filters reveal optimal normalization methods for different market participants.
Dynamic acquisition of features improves predictions with limited data.
Neural networks predict flow and elastic stresses in viscoelastic turbulence.
This paper designs sensor arrays for estimating unsteady flows efficiently.
Study optimal portfolios for traders with asymmetric information and delay.
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe et al. (2013) proposed to contrast measures of information flow obtained on the original data against the same measures obtained on time-rever…
Machine learning (ML) algorithms and machine learning based software systems implicitly or explicitly involve complex flow of information between various entities such as training data, feature space, validation set and results. Understanding the statistical distribution of such information and how they flow from one e…
Study reveals how investor flows impact stock prices, especially during herding episodes.
Unified framework maps financial market dynamics using TE and KM, revealing directional information flow.
The paper connects complex normalizing flows to Kähler-Ricci flows using geometric and statistical perspectives.
Study reveals how to determine area and curvature from fluid flow resonances.
Method learns PDE dynamics via evolving latent manifold using Ricci flow.
Unified framework for continuous-state discrete flow matching models.
The Information Bottleneck (IB) objective uses information theory to formulate a task-performance versus robustness trade-off. It has been successfully applied in the standard discriminative classification setting. We pose the question whether the IB can also be used to train generative likelihood models such as normal…
Enhances multi-modular models by directing information flow between components.
Paper proposes a novel method to reduce mutual information for missing data imputation.
Augmented bridge matching preserves coupling information between distributions.
New metric for probability measures connects physics and geometry.
BatchGFN uses generative flow networks for efficient batch active learning.
The space of probability densities is an infinite-dimensional Riemannian manifold, with Riemannian metrics in two flavors: Wasserstein and Fisher--Rao. The former is pivotal in optimal mass transport (OMT), whereas the latter occurs in information geometry---the differential geometric approach to statistics. The Rieman…