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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.

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149299448597 · Jun 202019922001200920172026
48 results for Hitting Times

Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental problem in the analysis of such networks is to properly define the similarity or dissimilarity between any two vertices. Despite the significance of this problem, statistical characterization of the proposed metrics …

2015-11-02abs ↗pdf ↗

Paper analyzes Hit-and-Run's convergence rates and applies similar methods to randomized Kaczmarz.

problem Quantifying advantages of Hit-and-Run's coordinate-free property.
method Sharp estimates via coupling methods and mixing time bounds.
result Ballistic and superdiffusive convergence rates in certain settings.

The paper analyzes McKean-Vlasov equations with hitting times, proving global solvability.

problem Analyzing blow-ups in McKean-Vlasov equations involving hitting times.
method Connection to the supercooled Stefan problem, comparison principles, and new transform.
result Proves global solvability for McKean-Vlasov dynamics under certain conditions.

SurvSurf predicts first hitting times for intermittent events without monotonic violations.

problem Predicting first hitting times for intermittent events with monotonicity guarantees.
method Partially monotonic neural network for sequential events, incorporating unobserved events.
result SurvSurf outperforms existing models in MSE and IBS metrics.

The paper simulates Lévy processes and their extremum and hitting time.

problem Simulating Lévy processes and their extremum and hitting time accurately and efficiently.
method Using characteristic functions and conditional characteristic functions, with conformal deformations and precalculated values on multi-grids.
result Accurate and fast simulation of Lévy processes and their extremum and hitting time.

This study compares two neural models for financial forecasting, showing their superiority.

problem Improving financial market trend predictions using neural networks.
method Systematic comparison of N-HiTS and N-BEATS with conventional models.
result N-HiTS and N-BEATS enhance forecast accuracy and robustness in financial time series data.

In this paper, we investigate the cooling-off effect (opposite to the magnet effect) from two aspects. Firstly, from the viewpoint of dynamics, we study the existence of the cooling-off effect by following the dynamical evolution of some financial variables over a period of time before the stock price hits its limit. S…

2018-03-26abs ↗pdf ↗

In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model…

2019-08-22abs ↗pdf ↗

We analyze the hitting time distributions of stock price returns in different time windows, characterized by different levels of noise present in the market. The study has been performed on two sets of data from US markets. The first one is composed by daily price of 1071 stocks trade for the 12-year period 1987-1998, …

2006-08-19abs ↗pdf ↗

Record companies invest billions of dollars in new talent around the globe each year. Gaining insight into what actually makes a hit song would provide tremendous benefits for the music industry. In this research we tackle this question by focussing on the dance hit song classification problem. A database of dance hit …

2019-05-17abs ↗pdf ↗

In this paper, we study the classical problem of the first passage hitting density of an Ornstein--Uhlenbeck process. We give two complementary (forward and backward) formulations of this problem and provide semi-analytical solutions for both. The corresponding problems are comparable in complexity. By using the method…

2018-10-04abs ↗pdf ↗

We study the problem of identity testing of markov chains. In this setting, we are given access to a single trajectory from a markov chain with unknown transition matrix QQ and the goal is to determine whether Q=PQ = P for some known matrix PP or Dist(P,Q)ε\text{Dist}(P, Q) \geq ε where Dist\text{Dist} is suitably defined. In r…

2019-02-06abs ↗pdf ↗

We introduce a simple stochastic volatility model, whose novelty consists in taking into account hitting times of the asset price, and study the optimal stopping problem corresponding to a put option whose time horizon (after the asset price hits a certain level) is exponentially distributed. We obtain explicit optimal…

2014-11-25abs ↗pdf ↗

This paper improves bond market making by adjusting hit-ratios for client flow quality.

problem Economic misleading of raw hit-ratios in corporate bond market making.
method Stochastic-control framework with residual-quality-adjusted hit-ratio.
result Optimal quotes decompose into various components, improving service/economics frontier.

In this paper we study the asymptotic decay of finite time ruin probabilities for an insurance company that faces heavy-tailed claims, uses predictable investment strategies and makes investments in risky assets whose prices evolve according to quite general semimartingales. We show that the ruin problem corresponds to…

2008-09-25abs ↗pdf ↗

The paper optimizes RV estimation by efficient sampling in time-changed diffusion models.

problem Improving realized variance (RV) estimation in time-changed diffusion models.
method Theoretical analysis and simulations of hitting time and realized business time sampling schemes.
result Realized business time sampling is empirically most efficient for high noise levels.

A new metric based on hitting probabilities for directed graphs and Markov chains.

problem Lack of metrics specifically adapted to asymmetric structure of directed graphs and Markov chains.
method Metric based on hitting probabilities, insensitive to shortest and average walk distances.
result New structural theory of directed graphs and utility for various applications.

We study the Stochastic Gradient Langevin Dynamics (SGLD) algorithm for non-convex optimization. The algorithm performs stochastic gradient descent, where in each step it injects appropriately scaled Gaussian noise to the update. We analyze the algorithm's hitting time to an arbitrary subset of the parameter space. Two…

2017-02-18abs ↗pdf ↗

Study bond market making with hit-ratio target using optimal control and HJB equations.

problem Optimizing bond market making with hit-ratio target in OTC markets.
method Stochastic optimal control approach, dualizing hit-ratio target, HJB equation, Riccati equation, linearization.
result Explicit quote decompositions into riskless spread, inventory-risk correction, and hit-ratio correction.

The hitting measure is singular and has dimension less than 1 for cocompact Fuchsian groups.

problem Analyzing the hitting measure and Hausdorff dimension for cocompact Fuchsian groups.
method Geometric and probabilistic analysis of random walks on cocompact Fuchsian groups.
result The hitting measure is singular with respect to Lebesgue measure and has a Hausdorff dimension strictly less than 1.

New algorithm for average reward learning with bounded hitting time assumption.

problem Minimizing regret in average reward reinforcement learning with bounded hitting time.
method Optimistic Q-learning with a novel L\overline{L} operator for bounded hitting time.
result Regret bound of ildeO(H5SAT) ilde{O}(H^5 S\sqrt{AT}) for average reward learning.

Paper investigates separating times for general diffusions, providing new insights.

problem Understanding phase transitions between equivalence and singularity in diffusions.
method Representation of separating time as hitting time of a deterministic set, characterized by speed and scale.
result Explicit and easy-to-check conditions for absolute continuity and singularity of diffusions.

KANEL combines models for early hit enrichment in virtual screening.

problem Assessing model accuracy in chemical bioactivity predictions.
method Ensemble workflow using Kolmogorov-Arnold Networks (KANs) and other models.
result Improves early hit enrichment metrics like PPV@N.

We propose a simpler derivation of the probability density function of Feller Diffusion using the Fourier Transform and solving the resulting equation via the Method of Characteristics. We also discuss simulation algorithms and confirm key properties related to hitting time probabilities via the simulation.

2019-05-26abs ↗pdf ↗

Derives integral representations for a Lévy process and its extremum, hitting time, with fast evaluation.

problem Efficiently evaluating the joint probability density function of a Lévy process, its supremum, and hitting time.
method Integral representations, Laplace-Fourier transforms, summation by parts, conformal deformation, trapezoid rules, Gaver-Wynn-Rho algorithm.
result Explicit calculations and fast evaluation of the joint cpdf for Lévy processes.

Adaptive denoising models adjust the number of steps based on noise level.

problem Generating data with lower intrinsic dimensions.
method Adaptive diffusion models using Doob's h-transform to terminate at a random time.
result Adaptive models simplify termination to a first-hitting rule, enhancing adaptability.

High throughput screening of compounds (chemicals) is an essential part of drug discovery [7], involving thousands to millions of compounds, with the purpose of identifying candidate hits. Most statistical tools, including the industry standard B-score method, work on individual compound plates and do not exploit cross…

2017-09-28abs ↗pdf ↗

A model for hit song prediction can be used in the pop music industry to identify emerging trends and potential artists or songs before they are marketed to the public. While most previous work formulates hit song prediction as a regression or classification problem, we present in this paper a convolutional neural netw…

2017-10-30abs ↗pdf ↗