arXiv research

Machine learning models predict brain age with systematic bias, corrected in this study.

We study the effects of non-systematic and systematic mortality risks on the required initial capital in a pension plan, in the presence of financial risks. We discover that for a pension plan with few members the impact of pooling on the required capital per person is strong, but non-systematic risk diminishes rapidly…

In this paper we generate and systematically classify all prime planar knotoids with up to 5 crossings. We also extend the existing list of knotoids in $S^2$ and add all knotoids with 6 crossings.

Study on NNs for forecasting time series with novel control variable combinations.

Detects systematic anomalies in consumer complaints using NLP.

A method for profiling systematic uncertainties in SBI using Factorizable Normalizing Flows.

Hybrid ML ensemble predicts market risk and generates alpha.

Transformers improve logical reasoning on longer proofs but struggle with length.

Neuro-symbolic agent learns systematic generalisation from formal instructions.

The effect of proportional transaction costs on systematically generated portfolios is studied empirically. The performance of several portfolios (the index tracking portfolio, the equally-weighted portfolio, the entropy-weighted portfolio, and the diversity-weighted portfolio) in the presence of dividends and transact…

Spotlight method finds hidden errors in deep learning models.

Study shows short exposure and systematic risk exposure affect disposition effect asymmetries.

Most previous works usually explained adversarial examples from several specific perspectives, lacking relatively integral comprehension about this problem. In this paper, we present a systematic study on adversarial examples from three aspects: the amount of training data, task-dependent and model-specific factors. Pa…

This short note provides a systematic construction of market models without unbounded profits but with arbitrage opportunities.

CLSVAE repairs systematic errors in images with minimal labeled data.

The paper models and prices cyber insurance risks, distinguishing idiosyncratic, systematic, and systemic risks.

In Part I of this series of papers, we made Riley's definition of Heckoid groups for 2-bridge links explicit, and gave a systematic construction of epimorphisms from 2-bridge link groups onto Heckoid groups, generalizing Riley's construction. In this paper, we give a complete characterization of upper-meridian-pair-pre…

We compare systematically several classes of stochastic volatility models of stock market fluctuations. We show that the long-time return distribution is either Gaussian or develops a power-law tail, while the short-time return distribution has generically a stretched-exponential form, but can assume also an algebraic …

The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way. In this work, we introduce a diagnostic benchmark suite, named CLUTRR, to clarify some key issues related to the robustness and systemati…

The constructions of the virtual Euler (or moduli) cycles and their properties are explained and developed systematically in the general abstract settings.

Method for creating synthetic multi-fidelity data sets.

A new framework for systematic graph neural network data augmentation.

We consider spectral sequences in smooth generalized cohomology theories, including differential generalized cohomology theories. The main differential spectral sequences will be of the Atiyah-Hirzebruch (AHSS) type, where we provide a filtration by the Cech resolution of smooth manifolds. This allows for systematic st…

LR-Robot accelerates SLRs by combining expert oversight and AI, revealing trends and patterns in financial research.

Systematic review of multimodal data challenges and solutions.

Systematic and multifactor risk models are revisited via methods which were already successfully developed in signal processing and in automatic control. The results, which bypass the usual criticisms on those risk modeling, are illustrated by several successful computer experiments.

We develop an algorithm for systematic design of a large artificial neural network using a progression property. We find that some non-linear functions, such as the rectifier linear unit and its derivatives, hold the property. The systematic design addresses the choice of network size and regularization of parameters. …

Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the benefits of locality in hardware, systematic scan is commonly used, even though most st…

Paper develops an AI-driven framework for systematic investing.

Analytical, free of time consuming Monte Carlo simulations, framework for credit portfolio systematic risk metrics calculations is presented. Techniques are described that allow calculation of portfolio-level systematic risk measures (standard deviation, VaR and Expected Shortfall) as well as allocation of risk down to…

Given a presentation for a rack $\mathcal R$, we define a process which systematically enumerates the elements of $\mathcal R$. The process is modeled on the systematic enumeration of cosets first given by Todd and Coxeter. This generalizes and improves the diagramming method for $n$-quandles introduced by Winker. We p…

Generative model calibrates 3D battery cathode morphologies from 2D images.

Economic factors significantly influence stock returns, as shown by attribution analysis.

Paper resolves bias in ALFT training using generalized alignment games.

HI-SIGMA improves sensitivity in high-dimensional statistical inference with data-driven background models.

Study uses TV news to measure climate risks affecting clean energy firms.

Extends ASRF model for green and brown loans, accounting for systematic and idiosyncratic risks.

Paper tackles division difficulty, proposing new methods to improve accuracy.

Paper analyzes systematic jump risk around the clock using news narratives.

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on Factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, w…

A general class of Lorentzian metrics, $M_0 x R^2$, $ds^2 = <.,.> + 2 du dv + H(x,u) du^2$, with $(M_0, <.,.>$ any Riemannian manifold, is introduced in order to generalize classical exact plane fronted waves. Here, we start a systematic study of their main geodesic properties: geodesic completeness, geodesic connected…

This study compares deep generative models to traditional methods for generating financial time series.

This paper reviews digital transformation research from 2011-2024, focusing on corporate finance.

Study examines how measurement errors impact clustering algorithms.

The insufficient understanding of the credit network structure was recognized as a key factor for regulators' underestimation of the destructive systematic risk during the financial crisis that started in 2007. The existing credit network research either took a macro perspective to clarify the topological properties of…

Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we …

Equity options are known to be notoriously difficult to price accurately, and even with the development of established mathematical models there are many assumptions that must be made about the underlying processes driving market movements. As such, the theoretical prices outputted by these models are often slightly di…

Systematic review of conformal inference for treatment effect estimation.