arXiv research

Extends Kyle model to multiple traders with different time-preference coefficients.

Extended model ensures long-term survival of traders in limited stock market participation.

The paper analyzes how mutable blockchain protocols affect miner behavior and strategic stability.

This paper considers the problem of consumption and investment in a financial market within a continuous time stochastic economy. The investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switch according to a finite…

Proposes a new consumption strategy based on martingale principles.

In a continuous time stochastic economy, this paper considers the problem of consumption and investment in a financial market in which the representative investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switches…

Study optimal control of diffusion processes with infimum or supremum costs.

Reinforcement learning (RL) typically defines a discount factor as part of the Markov Decision Process. The discount factor values future rewards by an exponential scheme that leads to theoretical convergence guarantees of the Bellman equation. However, evidence from psychology, economics and neuroscience suggests that…

Bayesian optimization learns DM preferences for multi-outcome experiments.

This paper considers a time-inconsistent stopping problem in which the inconsistency arises from non-constant time preference rates. We show that the smooth pasting principle, the main approach that has been used to construct explicit solutions for conventional time-consistent optimal stopping problems, may fail under …

The main goal of this paper is presentation a modern axiomatic approach to financial arithmetic. At the first, the axiomatic financial arithmetic theory was proposed by Peccati who has introduced the axiomatic definition of the future value. This theory has been extensively developed in past years. Proposed approach to…

New RL approach handles non-exponential discounting for sequential decisions.

This paper revisits optimal investment strategies for defined contribution pension schemes using forward preferences.

Enhances robo-advisors with client investment preference inference.

In the present paper, we identify several distributions from Physics and study their applicability to phenomena such as distribution of income, wealth, and expenditure. Firstly, we apply logistic distribution to these data and we find that it fits very well the annual data for the entire income interval including for u…

Study portfolio optimization with transaction costs and recursive preferences.

In this short report, we investigate the ability of the DCCA coefficient to measure correlation level between non-stationary series. Based on a wide Monte Carlo simulation study, we show that the DCCA coefficient can estimate the correlation coefficient accurately regardless the strength of non-stationarity (measured b…

Formula connects linking coefficients to Kontsevich integral coefficients.

Computed distortion coefficients for the α-Grushin plane.

Machine learning predicts Kronecker coefficients with high accuracy.

Abstract: Determines thermoelastic coefficients from boundary data.

New filling functions for groups with coefficients show different asymptotic behavior.

Abstract: Determines Lamé coefficients from boundary measurements.

Defines and proves properties of weighted renormalized volume coefficients.

Improved 3D LiDAR data classification using product coefficients.

High-dimensional, large-sample astrophysical databases of galaxy clusters, such as the Chandra Deep Field South COMBO-17 database, provide measurements on many variables for thousands of galaxies and a range of redshifts. Current understanding of galaxy formation and evolution rests sensitively on relationships between…

We study the structure of the stable coefficients of the Jones polynomial of an alternating link. We start by identifying the first four stable coefficients with polynomial invariants of a (reduced) Tait graph of the link projection. This leads us to introduce a free polynomial algebra of invariants of graphs whose ele…

Paper calculates third coefficient in Kaehler-Einstein metric expansion.

Develops local elliptic regularity for geometrically-natural operators with low regularity coefficients.

The paper uses Floer homology to study twist coefficients and their behavior after capping off.

Extends A-type coefficient polynomials to B-type setting, introducing new invariants.

We simplify complex regression coefficients using linearization and feature comparison.

Bounding twist number of surface links using polynomial coefficients.

Characterizes differential forms and vector fields with constant coefficients on manifolds.

Study on gradient descent in Hilbert spaces with Markov chains, focusing on mixing coefficients.

Recently, how to expand data transmission to reduce cell data and repeated cell transmission has received more and more research attention. In mobile social networks, content popularity prediction has always been an important part of traffic offloading and expanding data dissemination. However, current mainstream conte…

In this paper we use wavelet concepts to show that correlation coefficient between two financial data's is not constant but varies with scale from high correlation value to strongly anti-correlation value This studies is important because correlation coefficient is used to quantify degree of independence between two va…

The conformal powers of the Laplacian of a Riemannian metric which are known as the GJMS-operators admit a combinatorial description in terms of the Taylor coefficients of a natural second-order one-parameter family $\H(r;g)$ of self-adjoint elliptic differential operators. $\H(r;g)$ is a non-Laplace-type perturbation …

Guts determine the leading coefficients of $L^2$-Alexander torsions for 3-manifolds.

Survey of recent measures of association, including a new coefficient.

Diffusion models adapt to low-dimensional data regardless of coefficient choices.

Paper finds coefficients of Catalan states using Θ_A-state expansion.

GenMod uses generative models to approximate high-dimensional PDE solutions with limited evaluations.

Privacy amplification improved through contraction coefficients and $E_γ$-divergence.

We prove the $3$-manifold $\RP^3 \# \RP^3$ is of $\Z_{2}$-coefficient homology $(1, 2)$-systolic freedom. Given a Riemannian metric on $\RP^{3}\# \RP^{3}$, we define $\Z_{2}$-coefficient homology $1$-systole as the infimum of lengths of all nonseparating geodesic loops representing nontrivial classes in $H_{1}(\RP^3\#\…

The nullspace and regularization impact high-dimensional linear regression interpretability.

Framework uses deep learning and statistical models to solve PDEs with discontinuous coefficients.

A fundamental property of complex networks is the tendency for edges to cluster. The extent of the clustering is typically quantified by the clustering coefficient, which is the probability that a length-2 path is closed, i.e., induces a triangle in the network. However, higher-order cliques beyond triangles are crucia…