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.
Funds inflate their returns due to price pressure, leading to wealth reallocation and market crashes.
This paper presents a continuous-time model of intraday trading, pricing, and liquidity with dynamic TWAP and VWAP benchmarks. The model is solved in closed-form for the competitive equilibrium and also for non-price-taking equilibria. The intraday trajectories of TWAP trading targets cause predictable intraday pattern…
Model predicts trading strategies based on latent demand and price impact.
We study optimal liquidation of a trading position (so-called block order or meta-order) in a market with a linear temporary price impact (Kyle, 1985). We endogenize the pressure to liquidate by introducing a downward drift in the unaffected asset price while simultaneously ruling out short sales. In this setting the l…
This is the first paper that estimates the price determinants of BitCoin in a Generalised Autoregressive Conditional Heteroscedasticity framework using high frequency data. Derived from a theoretical model, we estimate BitCoin transaction demand and speculative demand equations in a GARCH framework using hourly data fo…
CapOptix uses options theory to price capacity in electricity markets.
The paper surveys pressure metrics in geometry and dynamics.
Paper introduces a new metric for deforming surfaces with parabolics.
This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pressure ulcers. It outlines the reasons why the project has been developed and provides a central point of contact for pressure ulcer analysi…
The study examines entropy and pressure at infinity in negatively curved manifolds, linking them to strong positive recurrence.
Deep learning speeds up pressure prediction in carbon storage reservoirs.
Study pressure metrics for cusped Hitchin representations.
The paper establishes pressure gaps for manifolds with flat subtori singularities.
Bitcoin option prices reflect both market maker supply and trader demand, especially from those with insider information.
Numerous studies have been carried out to measure wind pressures around circular cylinders since the early 20th century due to its engineering significance. Consequently, a large amount of wind pressure data sets have accumulated, which presents an excellent opportunity for using machine learning (ML) techniques to tra…
The study identifies features making cross-impact relevant in explaining price variance of US assets.
We prove that the pressure metric on the Teichmüller space of a bordered surface is incomplete and its partial completion can be given by the moduli space of metric graphs for a fat graph associated to the same bordered surface equipped with pressure metric. As a corollary, we show that the pressure metric is not a con…
The paper analyzes the pricing of a new compute futures asset.
Study develops a data-based model for in-cylinder pressure and cyclic variations in RCCI engines.
Study forecasts aortic pressure with deep learning models.
Central Counterparties (CCPs) are widely promoted as a requirement for safe banking with little dissent except on technical grounds (such as proliferation of CCPs). Whilst CCPs can have major operational positives, we argue that CCPs have many of the business characteristics of Rating Agencies, and face similar busines…
The paper analyzes how open-end fund sales affect prices and returns.
The paper studies Blaschke products, proving uniformization and non-degeneracy of pressure metrics.
Study minimizes market inefficiency in systemic economies.
Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this chal…
Optimal market making strategy with price forecasts reduces inventory costs and spreads.
The paper defines a path metric on a stable component of polynomial families.
Deep learning reconstructs pressure fields and classifies leakage rates in CCS storage sites.
The understanding of the dynamics of the velocity gradients in turbulent flows is critical to understanding various non-linear turbulent processes. The pressure-Hessian and the viscous-Laplacian govern the evolution of the velocity-gradients and are known to be non-local in nature. Over the years, several simplified dy…
In this paper, we analyzed the physical meaning of scalar curvatures for a generalized Riemannian space. It is developed the Madsen's formulae for pressures and energy-densities with respect to the corresponding energy-momentum tensors. After that, the energy-momentum tensors, pressures, energy-densities and state-para…
We prove that the Hitchin parametrization provides geodesic coordinates at the Fuchsian locus for the pressure metric in the Hitchin component of surface group representations into . The proof consists of the following elements: we compute first derivatives of the pressure metric…
In this paper, we extend the construction of pressure metrics to Teichmüller spaces of surfaces with punctures. This construction recovers Thurston's Riemannian metric on Teichmüller spaces. Moreover, we prove the real analyticity and the convexity of Manhattan curves of the finite area type-preserving Fuchsian represe…
In this article we construct the pressure form on the moduli space of higher dimensional Margulis spacetimes without cusps and study its properties. We show that the Margulis spacetimes are infinitesimally determined by their marked Margulis invariant spectrums. We use it to show that the restrictions of the pressure f…
Study investor attention using search volume data before and after mobile device popularity.
This paper defines the pressure metric on the Moduli space of Margulis spacetimes without cusps and shows that it is positive definite on the constant entropy sections. It also demonstrates an identity regarding the variation of the cross-ratios.
Study on MHD equilibria on curved spaces without symmetries.
Bayesian inference calibrates Hall thruster model uncertainty at varying pressures.
Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism…
We present a financial market model, characterized by self-organized criticality, that is able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. We consider a community of heterogeneous traders, composed by chartists and fundamentalists, and focus on the role of informative…
New framework detects crypto wash trading using liquidity measures.
Hybrid model predicts flow and pressure in water systems.
Soap films collapse only if their bulk has negative pressure, forming convex shapes.
ANN model predicts zinc leaching filter cake moisture accurately.
Proves closure for specific spacetimes with certain conditions.
We consider insurance derivatives depending on an external physical risk process, for example a temperature in a low dimensional climate model. We assume that this process is correlated with a tradable financial asset. We derive optimal strategies for exponential utility from terminal wealth, determine the indifference…
A new method splits surface flow discretizations into streamfunctions and harmonic fields.
Study equilibrium measures on manifolds without conjugate points with visibility covering.
Deep learning has been used in many areas, such as feature detections in images and the game of go. This paper presents a study that attempts to use the deep learning method to predict turbomachinery performance. Three different deep neural networks are built and trained to predict the pressure distributions of turbine…