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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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4.2%8.3%12.5%16.7% · Apr 199519922001200920172026
48 results for delay spread

Study extends Lévy models to capture market propagation delays.

problem Capturing sudden events in related markets with stochastic delays.
method Extend multivariate Lévy models using self-decomposability and multivariate subordination.
result Derived closed-form expressions for characteristic function and implemented Monte Carlo scheme.

Paper tackles delays in multi-agent reinforcement learning, improving performance.

problem Challenges in reinforcement learning due to delays in real-world systems.
method Proposes a novel framework for multi-agent reinforcement learning with delays, using Delay-Aware Markov Games and centralized-decentralized training.
result Demonstrates significant improvement in performance with delay-aware multi-agent reinforcement learning.

A new algorithm tackles delayed combinatorial semi-bandit with causal relations.

problem Optimizing decisions in a non-stationary environment with delayed and causally related rewards.
method Formalized as a non-stationary delayed combinatorial semi-bandit problem, the approach models causal relations with a directed graph in a stationary structural equation model. The agent learns these relations from delayed feedback to optimize decisions.
result Proved a regret bound for the proposed algorithm's performance.

Study non-oblivious adversarial bandits with delayed feedback and propose algorithms with improved regret bounds.

problem Adversarial bandit problem with delayed, composite anonymous feedback.
method Propose wrapper algorithm for non-oblivious delay setting, achieving o(T)o(T) policy regret.
result Achieve o(T)o(T) policy regret for many adversarial bandit problems with bounded memory loss sequences.

New algorithm for multi-armed bandits with delayed, partially observed rewards.

problem Sequential decision-making with delayed feedback.
method Proposed multi-armed bandits with generalized temporally-partitioned rewards, introducing β-spread property.
result Upper bound on performance of TP-UCB-FR-G algorithm improves state of the art.

Develops a new bivariate process for energy markets with improved simulation methods.

problem Modelling energy markets with stochastic delays and efficient simulations.
method Introduces a novel bivariate Normal Inverse Gaussian process and a path simulation scheme.
result Improves simulation efficiency for energy market models.

Wi-GATr learns to simulate wireless signals with high accuracy and speed.

problem Inaccurate wireless signal propagation models limit modern communication system design.
method Wi-GATr uses a Geometric Algebra Transformer to learn from scene primitives.
result Wi-GATr achieves more accurate predictions than existing methods.

We explore a novel setting of the Multi-Armed Bandit (MAB) problem inspired from real world applications which we call bandits with "stochastic delayed composite anonymous feedback (SDCAF)". In SDCAF, the rewards on pulling arms are stochastic with respect to time but spread over a fixed number of time steps in the fut…

2019-10-02abs ↗pdf ↗

Study evaluates different price response definitions for NASDAQ stocks.

problem Understanding the long-lasting effects of trading activity on stock prices.
method Examined two different price response implementations for NASDAQ Trades and Quotes (TAQ) data.
result Results are qualitatively the same for two different time scale definitions, but response can vary by up to a factor of two.

Study reveals how dengue spread patterns vary across different years in Recife, Brazil.

problem Understanding spatial organization of dengue transmission in urban areas.
method Spatial analysis of dengue cases using topological data analysis and Vietoris-Rips filtrations.
result Critical percolation thresholds define distinct geometric regimes of dengue spread.

Study of Polymarket's prediction market microstructure using tick-level order book data.

problem Understanding the microstructure of decentralized prediction markets.
method Analysis of a continuous tick-level order book feed and on-chain trade records.
result Trade direction inferred from Polymarket's public order-book feed disagrees with on-chain data in ~59% of cases.

Paper tackles action delays in reinforcement learning, proposing a delay-aware framework.

problem Action delays degrade reinforcement learning performance in real-world systems.
method Formal definition of delay-aware MDP, transformation into standard MDP with augmented states, delay-aware model-based reinforcement learning framework.
result Proposed framework is more efficient in training and transferable between systems with various delay durations.

New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.

problem Fair selection in stochastic combinatorial semi-bandit with delayed feedback.
method Introduced merit-based fairness constraints and new bandit algorithms for reward and fairness.
result Achieved sublinear expected reward and fairness regrets with dependence on delay distribution quantiles.

New algorithm handles delayed feedback robustly, reducing regret without knowing delay bounds.

problem Bandits with variably delayed feedback, especially excessive delays.
method Implicit exploration scheme, adaptive skipping, drifted regret control.
result Can tolerate arbitrary excessive delays up to order T, reducing regret.

Study on synchronization in financial markets with time delays.

problem Understanding market dynamics and synchronization in financial systems with time delays.
method Examined a system of coupled non-linear delay-differential equations, linearized for small delays, and analyzed collective dynamics using bifurcation diagrams and numerical solutions.
result Demonstrated that limit cycles can be maintained in coupled N-asset models with appropriate parameterization, leading to market synchronization.

New algorithm tackles stochastic bandits with varying arm-dependent delays.

problem Applying existing algorithms to stochastic delayed bandit settings is restricted by strong assumptions on delay distributions.
method Proposes a simple UCB-based algorithm called PatientBandits that weakens assumptions on delay distributions.
result Provides bounds on regret and performance lower bounds for the PatientBandits algorithm.

BayTiDe discovers time-delayed differential equations from noisy data.

problem Discovering time-delayed differential equations from data with large delays and noise.
method Bayesian inference with a sparsity-promoting prior.
result BayTiDe accurately identifies time-delayed differential equations with accuracy proportional to data resolution.

We investigate multiarmed bandits with delayed feedback, where the delays need neither be identical nor bounded. We first prove that "delayed" Exp3 achieves the O((KT+D)lnK)O(\sqrt{(KT + D)\ln K} ) regret bound conjectured by Cesa-Bianchi et al. [2019] in the case of variable, but bounded delays. Here, KK is the number of actio…

2019-06-03abs ↗pdf ↗

Poor economies not only produce less; they typically produce things that involve fewer inputs and fewer intermediate steps. Yet the supply chains of poor countries face more frequent disruptions---delivery failures, faulty parts, delays, power outages, theft, government failures---that systematically thwart the product…

2017-07-19abs ↗pdf ↗

New algorithm tackles non-stationary delayed feedback in recommender systems.

problem Challenges in learning from delayed feedback in non-stationary environments.
method Developed a UCRL-based algorithm for non-stationary, delayed bandits with intermediate observations.
result Sublinear regret guarantees for the proposed algorithm in non-stationary delayed environments.

PCTS optimizes noisy, delayed, multi-fidelity feedbacks in black-box optimization.

problem Optimizing unknown functions with noisy, delayed, and multi-fidelity feedbacks.
method ProCrastinated Tree Search (PCTS) with DUCB1 and DUCBV algorithms.
result PCTS achieves better regret bounds for delayed, noisy, and multi-fidelity feedbacks.

Predicting conversion rates (CVRs) in display advertising (e.g., predicting the proportion of users who purchase an item (i.e., a conversion) after its corresponding ad is clicked) is important when measuring the effects of ads shown to users and to understanding the interests of the users. There is generally a time de…

2018-02-01abs ↗pdf ↗

We study a variant of the stochastic KK-armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number…

2017-09-20abs ↗pdf ↗

Study online learning with delays and capacity constraints, achieving optimal regret bounds.

problem Online learning with delays and capacity constraints.
method Novel scheduling and preemptive techniques, matching upper and lower bounds.
result Achieves optimal regret bounds across all capacity levels.

Online learning with delayed feedback has received increasing attention recently due to its several applications in distributed, web-based learning problems. In this paper we provide a systematic study of the topic, and analyze the effect of delay on the regret of online learning algorithms. Somewhat surprisingly, it t…

2013-06-04abs ↗pdf ↗

Federated learning technique improves convergence speed with communication delays.

problem Communication delays between edge nodes and aggregator in federated learning.
method Developed FedDelAvg, a technique that generalizes federated averaging to incorporate a weighting between current local model and delayed global model.
result FedDelAvg achieves a significant improvement in convergence speed, especially when optimizing the weighting scheme to account for delays.

Study on unimodality of plucking polynomial with delay function.

problem Exploring unimodality of plucking polynomial with delay function.
method Presented a formula for the plucking polynomial of hedgehog rooted trees and explored unimodality with specific delay functions.
result Found interesting examples and speculations on unimodality of plucking polynomials with delay functions.

We propose a model to study the effects of delayed information on option pricing. We first talk about the absence of arbitrage in our model, and then discuss super replication with delayed information in a binomial model, notably, we present a closed form formula for the price of convex contingent claims. Also, we addr…

2017-07-06abs ↗pdf ↗

We study the optimal timing of derivative purchases in incomplete markets. In our model, an investor attempts to maximize the spread between her model price and the offered market price through optimally timing her purchase. Both the investor and the market value the options by risk-neutral expectations but under diffe…

2010-08-21abs ↗pdf ↗

Stochastic delay differential equations (SDDE's) have been used for financial modeling. In this article, we study a SDDE obtained by the equation of a CIR process, with an additional fixed delay term in drift; in particular, we prove that there exists a unique strong solution (positive and integrable) which we call fix…

2018-06-04abs ↗pdf ↗

New method estimates traffic congestion delays using statistical causality.

problem Accurate estimation of traffic congestion delays during accidents.
method Proposes a novel time delay estimation method using lag-specific transfer entropy (TE) and Markov bootstrap techniques.
result Validated the method's efficacy using simulated and real data.

Adapts two algorithms for online learning with delayed rewards.

problem Online learning with delayed rewards in generalized linear contextual bandits.
method Modifies upper confidence bounds and Thompson sampling algorithms for delayed rewards.
result Both algorithms can be made robust to delays, improving their performance.

Neural Laplace Control tackles offline RL for continuous-time delayed systems with irregular observations.

problem Offline reinforcement learning problems involving continuous-time environments with delays and irregular observations.
method Combines a Neural Laplace dynamics model with a model predictive control (MPC) planner.
result Achieves near expert policy performance on continuous-time delayed environments.