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arXiv research

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.

168,695 papers · 148 categories

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86172258344 · Jun 202019922001200920172026
48 results for decay order

Previous work of the authors establishes a criterion on the fundamental group of a knot complement that determines when Dehn surgery on the knot will have a fundamental group that is not left-orderable. We provide a refinement of this criterion by introducing the notion of a decayed knot; it is shown that Dehn surgery …

2011-03-11abs ↗pdf ↗

We study the geodesic X-ray transform on Cartan-Hadamard manifolds, and prove solenoidal injectivity of this transform acting on functions and tensor fields of any order. The functions are assumed to be exponentially decaying if the sectional curvature is bounded, and polynomially decaying if the sectional curvature de…

2017-05-29abs ↗pdf ↗

AdamNX improves Adam's stability by adjusting its learning rate.

problem Adam's tendency to converge to non-flat minima in large-scale models.
method Proposes a novel exponential decay mechanism for Adam's second-order moment estimate.
result AdamNX outperforms Adam and its variants in stability and performance.

Novel Adam-family method with decoupled weight decay for training neural networks.

problem Training nonsmooth neural networks with weight decay.
method Proposes a novel Adam-family method with decoupled weight decay, establishing convergence properties and demonstrating superior performance.
result Asymptotically approximates SGD and enhances generalization performance.

Paper tackles dynamic pricing in a geometrically decaying environment, achieving better occupancy with lower rates.

problem Minimizing expected loss in a dynamically changing environment with decisions dependent on the data distribution.
method Introduces algorithms for information and loss function settings, using repeated decision deployment to allow mixing of the environment.
result Iteration complexity matches first and zero order stochastic gradient methods up to logarithmic factors.

Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of L2L_2 regularization. Literal weight decay has been shown to outperform L2L_2 regularization for…

2018-10-29abs ↗pdf ↗

Using a proprietary dataset of meta-orders and prediction signals, and assuming a quasi-linear impact model, we deconvolve market impact from past correlated trades and a predictable return component to elicit the temporal dependence of the market impact of a single daily meta-order, over a ten day horizon in various e…

2014-07-12abs ↗pdf ↗

In this paper, we use a database of around 400,000 metaorders issued by investors and electronically traded on European markets in 2010 in order to study market impact at different scales. At the intraday scale we confirm a square root temporary impact in the daily participation, and we shed light on a duration factor …

2014-11-30abs ↗pdf ↗

Riemannian stochastic gradient descent converges faster with increasing batch size.

problem Improving convergence rate of Riemannian stochastic gradient descent.
method Theoretical analysis and numerical investigation of increasing batch size effects.
result Riemannian stochastic gradient descent converges faster with increasing batch size.

We show that axisymmetric extremal horizons are unstable under linear scalar perturbations. Specifically, we show that translation invariant derivatives of generic solutions to the wave equation do not decay along such horizons as advanced time tends to infinity, and in fact, higher order derivatives blow up. This resu…

2012-06-28abs ↗pdf ↗

This article provides a novel framework to evaluate limit order tactics that highlights expected fill price, adverse price selection cost, and opportunity cost. We formulate the problem of optimal execution of market orders with nonlinear market impact, power law decay kernel, and stochastic and deterministic liquidity…

2014-09-04abs ↗pdf ↗

Optimizes trading strategy considering alpha decay and transaction costs.

problem Maximizing reward in a multi-period portfolio with transaction costs and alpha decay.
method Formulated as an infinite horizon Markov Decision Process, solved using a modified value iteration algorithm with convergence proof and asymptotic analysis.
result Characterized optimal trading policy that maximizes average expected reward.

In this paper we study the behaviour of the continuous spectrum of the Laplacian on a complete Riemannian manifold of bounded curvature under perturbations of the metric. The perturbations that we consider are such that its covariant derivatives up to some order decay with some rate in the geodesic distance from a fixe…

2007-01-10abs ↗pdf ↗

We describe a Markov latent state space (MLSS) model, where the latent state distribution is a decaying mixture over multiple past states. We present a simple sampling algorithm that allows to approximate such high-order MLSS with fixed time and memory costs.

2017-11-08abs ↗pdf ↗

We introduce a class of "weakly asymptotically hyperbolic" geometries whose sectional curvatures tend to 1-1 and are C0C^0, but are not necessarily C1C^1, conformally compact. We subsequently investigate the rate at which curvature invariants decay at infinity, identifying a conformally invariant tensor which serves a…

2015-06-10abs ↗pdf ↗

This paper investigates the effectiveness of decoupled weight decay at the start of training.

problem The traditional approach to weight decay is not effective throughout training.
method The authors investigate decoupled weight decay, applying it only at the start of training.
result Applying weight decay only at the start of training stabilizes network weights and improves performance.

LOBRM model recreates limit order books from trade and quote data.

problem Lack of LOB data and limitations in LOBRM model.
method Extended LOBRM with time-weighted z-score standardization and exponential decay kernel, conducted in chronological order.
result LOBRM with decay kernel outperforms traditional models and module ensembling is effective.

We prove the existence of a C1,1C^{1,1} conformally compact Einstein metric on the ball that has asymptotic sectional curvature decay to 1-1 plus terms of order e2re^{-2r} where rr is the distance from any fixed compact set. This metric has no C2C^2 conformal compactification.

2017-01-05abs ↗pdf ↗

SGD's performance improves with critical batch size, minimizing SFO complexity.

problem Optimizing SGD's performance with batch size and learning rate.
method Analysis of SGD using constant and decaying learning rates, focusing on batch size effects.
result SGD with critical batch size minimizes SFO complexity.

Trading algorithms that execute large orders are susceptible to exploitation by order anticipation strategies. This paper studies the influence of order anticipation strategies in a multi-investor model of optimal execution under transient price impact. Existence and uniqueness of a Nash equilibrium is established unde…

2016-09-02abs ↗pdf ↗

Geometric focusing affects dispersive estimates for Schrödinger and wave equations.

problem Long-time decay rate in dispersive estimates for Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones.
method Classifying the long-time decay rate in dispersive estimates for the Schrödinger and wave equations on non-trapping asymptotically conic manifolds and exact metric cones in terms of the intensity of geometric focusing.
result Each multiplicity of conjugate points within distance π on Y = ∂X0 leads to a |t|1/2-loss in the long-time decay order and a half-order shift in the regularity index in the dispersive estimate for the Schrödinger equation.

The study analyzes prediction errors in systems with memory kernels, providing bounds and stability results.

problem Prediction errors in stochastic dynamical systems with memory kernels.
method Analysis of generalized Langevin equations (GLEs) with Volterra equations, integrating synchronized noise coupling and weighted norms.
result Prediction discrepancies decay at a rate determined by the memory kernel's decay, quantitatively bounded by kernel estimation errors.

New height estimate for area minimizing currents, leading to unique tangent cones and decay properties.

problem Analyzing singularities of area minimizing currents.
method Height estimate, decay estimates, techniques inspired by previous works.
result Locally area minimizing currents have a unique tangent cone at almost every point and decay rapidly to a unique tangent plane at branch points.

Sharp inequalities for radial functions on hyperbolic spaces without boundary conditions.

problem Establishing inequalities for radial functions on hyperbolic spaces without zero boundary conditions.
method Novel approach considering both bounded and unbounded domains, focusing on weighted Sobolev and Adams-Trudinger-Moser embeddings.
result Theorems 1.2, 1.3, and 1.4 for weighted Sobolev embedding theorems, and Theorems 1.5 and 1.6 for Adams-Trudinger-Moser type embedding theorems.