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.
Language models can predict numeric values as strings.
Paper introduces NumLLM for better financial text understanding with numeric variables.
This paper presents several numerical applications of deep learning-based algorithms that have been introduced in [HPBL18]. Numerical and comparative tests using TensorFlow illustrate the performance of our different algorithms, namely control learning by performance iteration (algorithms NNcontPI and ClassifPI), contr…
Recognizing written domain numeric utterances (e.g. I need $1.25.) can be challenging for ASR systems, particularly when numeric sequences are not seen during training. This out-of-vocabulary (OOV) issue is addressed in conventional ASR systems by training part of the model on spoken domain utterances (e.g. I need one …
We deliver a call to arms for probabilistic numerical methods: algorithms for numerical tasks, including linear algebra, integration, optimization and solving differential equations, that return uncertainties in their calculations. Such uncertainties, arising from the loss of precision induced by numerical calculation …
Neural ODEs' performance varies with numerical method, requiring adaptive step size control.
LogEI improves Bayesian optimization by simplifying numerical computation of EI and related functions.
A new framework improves tensor completion accuracy by considering numerical priors.
LightOn OPUs accelerate randomized numerical linear algebra, reducing computational costs.
Study uses neural networks to solve complex equations efficiently.
New method improves stability of Gaussian process approximations.
Calibration of stochastic local volatility (SLV) models to their underlying local volatility model is often performed by numerically solving a two-dimensional non-linear forward Kolmogorov equation. We propose a novel finite volume (FV) discretization in the numerical solution of general 1D and 2D forward Kolmogorov eq…
We perform a massive evaluation of neural networks with architectures corresponding to random graphs of various types. We investigate various structural and numerical properties of the graphs in relation to neural network test accuracy. We find that none of the classical numerical graph invariants by itself allows to s…
New metrics reveal oversmoothing in GNNs more accurately than traditional methods.
The paper compares numerical schemes for nonholonomic systems using retraction maps.
Efficient method for vertex embedding and community detection.
This paper tackles ranking-based performance normalization for optimization algorithms.
Probabilistic numerics expands numerical tasks with black box methods.
Study identifies numerical signs of blow-up in hydrodynamic equations.
A new PCA method using T-norm outperforms existing methods.
Improved hypothesis testing and change-point detection using diffusion-based methods.
Enhances FM models for numerical features using function basis encoding.
LLMs struggle with arithmetic tasks unless they use high numerical precision.
The paper analyzes and improves the learning rates of distributed kernel ridge regression.
New method smooths integrands for efficient option pricing.
Agent Trading Arena trains LLMs in real-time financial markets to improve numerical reasoning.
Study validates Libor model for insurance benefits calculation.
In this article, we propose an exact simulation method of the Wishart multidimensional stochastic volatility (WMSV) model, which was recently introduced by Da Fonseca et al. \cite{DGT08}. Our method is based onanalysis of the conditional characteristic function of the log-price given volatility level. In particular, we…
Combines normalizing flows and quasi-Monte Carlo for improved numerical integration.
Space mapping speeds up shape optimization for PDEs.
NCG methods improve shape optimization efficiency.
We consider rate swaps which pay a fixed rate against a floating rate in presence of bid-ask spread costs. Even for simple models of bid-ask spread costs, there is no explicit strategy optimizing an expected function of the hedging error. We here propose an efficient algorithm based on the stochastic gradient method to…
We propose a new method for the numerical solution of backward stochastic differential equations (BSDEs) which finds its roots in Fourier analysis. The method consists of an Euler time discretization of the BSDE with certain conditional expectations expressed in terms of Fourier transforms and computed using the fast F…
Observations depending on sums of random variables are common throughout many fields; however, no efficient solution is currently known for performing max-product inference on these sums of general discrete distributions (max-product inference can be used to obtain maximum a posteriori estimates). The limiting step to …
We present effective numerical algorithms for locally recovering unknown governing differential equations from measurement data. We employ a set of standard basis functions, e.g., polynomials, to approximate the governing equation with high accuracy. Upon recasting the problem into a function approximation problem, we …
A new scheme for FBSDEs simplifies computation without Monte Carlo.
Novel method for estimating SIRD model parameters and forecasting COVID-19 deaths in Poland.
We present a new methodology of computing incremental contribution for performance ratios for portfolio like Sharpe, Treynor, Calmar or Sterling ratios. Using Euler's homogeneous function theorem, we are able to decompose these performance ratios as a linear combination of individual modified performance ratios. This a…
The L1-regularized maximum likelihood estimation problem has recently become a topic of great interest within the machine learning, statistics, and optimization communities as a method for producing sparse inverse covariance estimators. In this paper, a proximal gradient method (G-ISTA) for performing L1-regularized co…
This paper performs the numerical analysis and the computation of a Spread option in a market with imperfect liquidity. The number of shares traded in the stock market has a direct impact on the stock's price. Thus, we consider a full-feedback model in which price impact is fully incorporated into the model. The price …
Area under ROC curve (AUC) is a widely used performance measure for classification models. We propose two new distributionally robust AUC maximization models (DR-AUC) that rely on the Kantorovich metric and approximate the AUC with the hinge loss function. We consider the two cases with respectively fixed and variable …
We present a numerical approach for solving the free boundary problem for the Black-Scholes equation for pricing American style of floating strike Asian options. A fixed domain transformation of the free boundary problem into a parabolic equation defined on a fixed spatial domain is performed. As a result a nonlinear t…
We consider a structural model where the survival/default state is observed together with a noisy version of the firm value process. This assumption makes the model more realistic than most of the existing alternatives, but triggers important challenges related to the computation of conditional default probabilities. I…
New approach improves computational efficiency of Bass Local Volatility model.
New methods improve deep learning for solving linear PDEs.
A new method learns Hamiltonian functions from noisy data.
New method preserves spectral clustering performance under aggressive sparsification and quantization.
Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the convention…