arXiv research

Study develops sector rotation models using factor and fundamental analysis.

Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.

Abstract reviews recent Lagrangian analysis on immersions into higher dimensions.

Study uses machine learning to predict stock trends based on fundamental data.

AI models predict stock trends using historical data and public sentiment.

H-GAT improves stock selection by capturing complex higher-order stock relations and integrating both technical and fundamental analysis.

The informational context is regularly questioned in a transitional economic regime like the one implemented in China or Vietnam. This article investigates this issue and the predictive power of fundamental analysis in such context and more precisely in a Chinese context with an analysis of 3 different industries (medi…

This paper surveys some recent developments in fundamental limits and optimal algorithms for network analysis. We focus on minimax optimal rates in three fundamental problems of network analysis: graphon estimation, community detection, and hypothesis testing. For each problem, we review state-of-the-art results in the…

Topological Data Analysis is a recent and fast growing field providing a set of new topological and geometric tools to infer relevant features for possibly complex data. This paper is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of \tda\ for non experts.

Develops analysis of weak immersions with bounded second fundamental forms in critical Sobolev space.

Study spectral analysis on lens spaces, proving isospectral lens spaces with prime order fundamental groups.

The fundamental purpose of the present research article is to introduce the basic principles of Dimensional Analysis in the context of the neoclassical economic theory, in order to apply such principles to the fundamental relations that underlay most models of economic growth. In particular, basic instruments from Dime…

Study of circle configurations in the plane, proving aspherical space and computing fundamental groups.

On a periodic basis, publicly traded companies are required to report fundamentals: financial data such as revenue, operating income, debt, among others. These data points provide some insight into the financial health of a company. Academic research has identified some factors, i.e. computed features of the reported d…

Study uses LLMs to generate investor briefs from company reports and SEC filings.

No nontrivial automorphisms for cubic surfaces moduli space.

Analyzes Indian chemical industry post-Covid.

In this paper, we obtain fundamental $\mathcal{L}_{p}$ bounds in sequential prediction and recursive algorithms via an entropic analysis. Both classes of problems are examined by investigating the underlying entropic relationships of the data and/or noises involved, and the derived lower bounds may all be quantified in…

Paper uses machine learning for stock prediction using fundamental data.

We provide a critical analysis of the proof of the fundamental theorem of asset pricing given in the paper "Arbitrage and approximate arbitrage: the fundamental theorem of asset pricing" by B. Wong and C.C. Heyde (Stochastics, 2010) in the context of incomplete Itô-process models. We show that their approach can only w…

We carry out the harmonic analysis on four Platonic spherical three-manifolds with different topologies. Starting out from the homotopies (Everitt 2004), we convert them into deck operations, acting on the simply connected three-sphere as the cover, and obtain the corresponding variety of deck groups. For each topology…

The study improves fundamental gap estimates for surfaces with non-constant positive curvature.

MarketSenseAI uses LLMs to improve stock analysis and outperforms benchmarks.

Study reveals fundamental group properties of manifolds with specific curvature and growth.

Principal Component Analysis can be performed over small domains of an embedded Riemannian manifold in order to relate the covariance analysis of the underlying point set with the local extrinsic and intrinsic curvature. We show that the volume of domains on a submanifold of general codimension, determined by the inter…

Probabilistic method proves gap estimates on sphere.

For each Cantor set C in $R^{3}$, all points of which have bounded local genus, we show that there are infinitely many inequivalent Cantor sets in $R^{3}$ with complement having the same fundamental group as the complement of C. This answers a question from Open Problems in Topology and has as an application a simple c…

In this paper, we examine the fundamental performance limits of prediction, with or without side information. More specifically, we derive generic lower bounds on the $\mathcal{L}_p$ norms of the prediction errors that are valid for any prediction algorithms and for any data distributions. Meanwhile, we combine the ent…

Let $M$ be a compact Riemannian manifold with boundary $\pp M$ and $L= \DD+Z$ for a $C^1$-vector field $Z$ on $M$. Several equivalent statements, including the gradient and Poincaré/log-Sobolev type inequalities of the Neumann semigroup generated by $L$, are presented for lower bound conditions on the curvature of $L$ …

The geometry and analysis on Finsler manifolds is a very important part of Finsler geometry. In this article, we introduce some important and fundamental topics in global Finsler geometry and discuss the related properties and the relationships in them. In particular, we optimize and improve the various definitions of …

Analyzes Indian commercial dynamism using time series data.

Enhances financial analysis with multi-agent collaboration.

The study extends kernel universality to Riemannian symmetric spaces.

Transforms curves and surfaces for efficient geometric analysis.

Improved stock selection through predictive fundamentals and uncertainty estimates.

LLMs perform well in financial sentiment analysis without fine-tuning.

Study finds CNNs perform better with financial ratio data than fundamental data.

Study slopes on knot manifolds to understand their fundamental groups.

We perform global and local analysis of oscillatory and damped spherically symmetric fundamental solutions for Helmholtz operators $\big({-}Δ\pmβ^2\big)$ in $d$-dimensional, $R$-radius hyperbolic ${\mathbf H}_R^d$ and hyperspherical ${\mathbf S}_R^d$ geometry, which represent Riemannian manifolds with positive constant…

This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and mul…

Develops a Barta theorem for p-Laplacian on manifolds.

Networks are a fundamental model of complex systems throughout the sciences, and network datasets are typically analyzed through lower-order connectivity patterns described at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, also called network motifs, de…

Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a …

The paper proves rigidity results for self-shrinkers and surfaces with parallel weighted mean curvature.

Paper disproves Milnor's conjecture about manifold fundamental groups.

New method improves PCA for high-dimensional data with n < p.

Teaching tool simplifies Monte Carlo simulation for project risk analysis.

The book explores alternatives to worst-case analysis for algorithm performance.