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.
A new method treats all variables equally in fitting data.
In the context of fake news, bias, and propaganda, we study two important but relatively under-explored problems: (i) trustworthiness estimation (on a 3-point scale) and (ii) political ideology detection (left/right bias on a 7-point scale) of entire news outlets, as opposed to evaluating individual articles. In partic…
System constructs public competitor graph from financial reports.
A new framework for systematic graph neural network data augmentation.
We identify and optimize the fairness-accuracy tradeoff through TAF Curves and FAUC metrics.
The coefficient of determination, known as , is commonly used as a goodness-of-fit criterion for fitting linear models. is somewhat controversial when fitting nonlinear models, although it may be generalised on a case-by-case basis to deal with specific models such as the logistic model. Assume we are fittin…
The paper studies efficient Hessian fitting methods for stochastic optimization.
The Bezier simplex fitting is a novel data modeling technique which exploits geometric structures of data to approximate the Pareto front of multi-objective optimization problems. There are two fitting methods based on different sampling strategies. The inductive skeleton fitting employs a stratified subsampling from e…
We propose a data aggregation-based algorithm with monotonic convergence to a global optimum for a generalized version of the L1-norm error fitting model with an assumption of the fitting function. The proposed algorithm generalizes the recent algorithm in the literature, aggregate and iterative disaggregate (AID), whi…
CTEF fits ellipsoids to noisy data in any dimension.
Missing values, irregularly collected samples, and multi-resolution signals commonly occur in multivariate time series data, making predictive tasks difficult. These challenges are especially prevalent in the healthcare domain, where patients' vital signs and electronic records are collected at different frequencies an…
Algorithm decides if pseudo-Anosov flows have perfect fits.
We study a resource utilization scenario characterized by intrinsic fitness. To describe the growth and organization of different cities, we consider a model for resource utilization where many restaurants compete, as in a game, to attract customers using an iterative learning process. Results for the case of restauran…
Smoothed fitness landscape improves protein optimization.
JAXFit speeds up curve fitting on GPUs.
Kernel Multigrid accelerates Back-fitting for additive Gaussian Processes.
The accumulation of individual fitness or wealth is modelled as a population game in which pairs of individuals are recurrently and randomly matched to play a game over a resource. In addition, all individuals have random access to a constant background resource, and their fitness or wealth depreciates over time. For b…
A framework for eliciting utility functions from investor preferences.
Closed form formulas for swaption prices in HJM model are derived. These formulas are used for nonparametric fit of deterministic forward volatility. It is demonstrated that this formula and non-parametric fit works very well and can be used to identify arbitrage opportunities
This paper illustrates a procedure for fitting financial data with -stable distributions. After using all the available methods to evaluate the distribution parameters, one can qualitatively select the best estimate and run some goodness-of-fit tests on this estimate, in order to quantitatively assess its quality. I…
Paper proposes a perfect-fit model for CDO tranches.
Study shows how varying levels of supervision and orthonormality constraints affect generalization errors in subspace fitting.
Study presents MMC model for better fitting multiple choice data.
Paper uses DRL to improve volatility fitting in equity derivatives.
Study evaluates different mathematical models for three case studies using statistical fitting.
This paper is a step-by-step tutorial for fitting a mixture distribution to data. It merely assumes the reader has the background of calculus and linear algebra. Other required background is briefly reviewed before explaining the main algorithm. In explaining the main algorithm, first, fitting a mixture of two distribu…
In quantitative finance, we often fit a parametric semimartingale model to asset prices. To ensure our model is correct, we must then perform goodness-of-fit tests. In this paper, we give a new goodness-of-fit test for volatility-like processes, which is easily applied to a variety of semimartingale models. In each cas…
Finitely many pseudo-Anosov flows without perfect fits in a 3-manifold.
New method extends fitted Q-evaluation for distributional off-policy reinforcement learning.
Fitting models for non-Poisson point processes is complicated by the lack of tractable models for much of the data. By using large samples of independent and identically distributed realizations and statistical learning, it is possible to identify absence of fit through finding a classification rule that can efficientl…
A new kernel Stein test assesses fit for variable-length sequential data.
New findings show a balance between data fit and complexity in kernel hyperparameters.
New robustness test for kernel goodness-of-fit tests.
Neural networks fit fewer samples than their parameters suggest in practice.
FORE evaluates occupancy ratios without requiring Bellman completeness.
Optimal unimodal fitting for linear loss functions in a sequential, efficient manner.
The log-periodic power law (LPPL) is a model of asset prices during endogenous bubbles. If the on-going development of a bubble is suspected, asset prices can be fit numerically to the LPPL law. The best solutions can then indicate whether a bubble is in progress and, if so, the bubble critical time (i.e., when the bub…
Paper explores how text generation quality and diversity metrics relate to distribution fitting.
We show that univariate and symmetric multivariate Hawkes processes are only weakly causal: the true log-likelihoods of real and reversed event time vectors are almost equal, thus parameter estimation via maximum likelihood only weakly depends on the direction of the arrow of time. In ideal (synthetic) conditions, test…
We present and evaluate the Fast (conditional) Independence Test (FIT) -- a nonparametric conditional independence test. The test is based on the idea that when , is not useful as a feature to predict , as long as is also a regressor. On the contrary, if $P(X \mid Y, Z) \neq P(X…
In regression modelling approach, the main step is to fit the regression line as close as possible to the target variable. In this process most algorithms try to fit all of the data in a single line and hence fitting all parts of target variable in one go. It was observed that the error between predicted and target var…
Optimistic estimate predicts best fitting performance of nonlinear models.
New spectral tests assess network model fits efficiently.
Hawkes processes have seen a number of applications in finance, due to their ability to capture event clustering behaviour typically observed in financial systems. Given a calibrated Hawkes process, of concern is the statistical fit to empirical data, particularly for the accurate quantification of self- and mutual-exc…
We present techniques for effective Gaussian process (GP) modelling of multiple short time series. These problems are common when applying GP models independently to each gene in a gene expression time series data set. Such sets typically contain very few time points. Naive application of common GP modelling techniques…
Two methods monitor high-dimensional processes via manifold fitting or learning.
The paper uses FRFT to fit GTS distribution to asset returns.
New method uses neural networks to estimate parameters without needing detector simulations.