Study develops sector rotation models using factor and fundamental analysis.
problem Understanding and predicting sector shifts in financial markets.
method Systematic sector classification, factor analysis, and fundamental metrics evaluation.
result Developed predictive models with notable predictive capabilities.
Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.
problem Inaccurate financial analysis by LLMs leading to investment and regulatory issues.
method Proposes FinAR-Bench, a benchmark dataset with three steps: key info extraction, financial indicator calculation, and logical reasoning.
result LLMs perform better in key info extraction and indicator calculation but struggle with logical reasoning.
Abstract reviews recent Lagrangian analysis on immersions into higher dimensions.
problem Analyzing Lagrangians on immersions into higher dimensions.
method Reviews recent progress on Lagrangians on immersions with first and second fundamental forms and their derivatives.
result Recent progress in the analysis of Lagrangians on immersions into higher dimensions.
Study uses machine learning to predict stock trends based on fundamental data.
problem Predicting stock trends using fundamental analysis.
method Used LSTM, 1D CNN, and LR models on financial data.
result Logistic Regression models outperformed other models.
AI models predict stock trends using historical data and public sentiment.
problem Improving stock market prediction accuracy using AI.
method Employed regression and classification ML algorithms for technical and fundamental analysis respectively.
result Median performance suggests AI is not yet superior to stock markets.
H-GAT improves stock selection by capturing complex higher-order stock relations and integrating both technical and fundamental analysis.
problem Stock selection difficulty and lack of comprehensive analysis.
method Higher-order Graph Attention Network (H-GAT) that incorporates both technical and fundamental analysis.
result H-GAT outperforms existing methods in stock selection metrics.
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.
problem Analyzing weak immersions with bounded second fundamental forms in a critical Sobolev space.
method Develops analysis of Lipschitz immersions with bounded second fundamental forms in W2n−1,2 space. result Proves existence of C1 differential structure from weak immersions with bounded second fundamental forms. Study spectral analysis on lens spaces, proving isospectral lens spaces with prime order fundamental groups.
problem Spectral analysis of the Kohn Laplacian on lens spaces.
method Analog of Weyl's law and isospectral lens spaces with prime order fundamental groups.
result Two 3D lens spaces with prime order fundamental groups are isospectral with respect to the Kohn Laplacian if and only if they are CR isometric.
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.
problem Understanding the space of configurations of circles in the plane.
method Proved the space is aspherical and computed fundamental groups of its components.
result Fundamental groups are iterated semidirect products of braid groups, with structure dictated by a finite rooted tree.
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.
problem Improving data analysis for individual investors.
method Preprocessed data, used gpt-4o model in RAG regime, evaluated by investors.
result LLMs can generate useful investor briefs from company reports and SEC filings.
No nontrivial automorphisms for cubic surfaces moduli space.
problem Understanding automorphisms of cubic surfaces moduli space.
method Analyzing the fundamental group of the moduli space.
result No nontrivial biholomorphic automorphisms for cubic surfaces moduli space.
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
In this paper, we obtain fundamental Lp 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.
problem Predicting stock prices using fundamental data.
method Used three machine learning algorithms (FNN, RF, ANFIS) and feature selection for stock prediction.
result Random Forest (RF) model achieved the best prediction results.
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.
problem Estimating the fundamental gap for surfaces with non-constant positive curvature.
method Using a two-point maximum principle, the study establishes log-concavity and fundamental gap estimates.
result Corresponding log-concavity and fundamental gap estimates for surfaces with non-constant positive curvature are derived.
MarketSenseAI uses LLMs to improve stock analysis and outperforms benchmarks.
problem Improving accuracy in stock analysis and selection.
method Combining LLMs with SEC filings, earnings calls, and macroeconomic reports.
result Significant improvement in fundamental analysis accuracy and outperformance of benchmarks.
Study reveals fundamental group properties of manifolds with specific curvature and growth.
problem Understanding the fundamental groups of manifolds with nonnegative Ricci curvature and linear volume growth.
method Analysis of covering spaces and rigidity results for RCD spaces.
result Fundamental groups of manifolds contain subgroups of finite index or are finite.
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.
problem Proving fundamental gap estimates for Schrödinger operators on spheres.
method Reflection coupling method on Riemannian manifolds.
result Extends probabilistic proof to sphere, generalizing previous results.
For each Cantor set C in R3, all points of which have bounded local genus, we show that there are infinitely many inequivalent Cantor sets in R3 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…
Let M be a compact Riemannian manifold with boundary $\pp M$ and $L= \DD+Z$ for a C1-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.
problem Understanding commercial dynamism in India.
method Time series analysis of various economic indicators.
result Detailed insights into growth rate, trade balance, etc.
Enhances financial analysis with multi-agent collaboration.
problem Limited use of AI-agent collaboration in financial research.
method Proposes a multi-agent system for financial investment research.
result Multi-agent system outperforms single-agent models.
The study extends kernel universality to Riemannian symmetric spaces.
problem Understanding kernel universality in non-Euclidean domains.
method Harmonic analysis on Riemannian symmetric spaces.
result Proves universality of recent kernels on Riemannian symmetric spaces.
Transforms curves and surfaces for efficient geometric analysis.
problem Efficiently analyzing and comparing curves and surfaces.
method Square root velocity transformation for curves and intrinsic comparison for surfaces.
result Fundamental geometric properties of curves under the transformation.
The paper sets limits on prediction accuracy and generalization.
problem Fundamental limits of prediction accuracy and generalization.
method Combining entropic analysis and innovations approach.
result Conditions for achieving prediction error bounds.
Improved stock selection through predictive fundamentals and uncertainty estimates.
problem Selecting stocks based on future financial data to outperform traditional factor models.
method Train deep nets to forecast future fundamentals, incorporate uncertainty estimates, and adjust portfolios to manage risk.
result Simulated annualized return of 17.7% and Sharpe ratio of 0.84 for uncertainty-aware model, significantly higher than 14.0% and 0.52 for standard factor models.
LLMs perform well in financial sentiment analysis without fine-tuning.
problem Challenges in financial terminology, emotions, and ambiguous expressions.
method In-context learning methods for financial document-sentiment pairs.
result LLMs can generalize in-context demonstrations to new financial documents.
Study finds CNNs perform better with financial ratio data than fundamental data.
problem Improving CNN performance with financial data.
method Developed and analyzed three image encoding methods for financial data.
result Image encoding methods improve CNN performance for financial ratio data but not significantly for fundamental data.
Study slopes on knot manifolds to understand their fundamental groups.
problem Characterize slopes on knot manifolds to determine fundamental group properties.
method Develops new order-detection notions, parallels existing slope detection methods, and uses dynamics of 3-manifold group actions.
result Conjectured structure theorems connecting Heegaard-Floer homology and foliation dynamics to left-orderability.
We perform global and local analysis of oscillatory and damped spherically symmetric fundamental solutions for Helmholtz operators (−Δ±β2) in d-dimensional, R-radius hyperbolic HRd and hyperspherical SRd 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.
problem Sharp lower bounds for p-fundamental tone on Riemannian manifolds.
method Extends Barta-type formulation to nonlinear setting on Riemannian manifolds.
result Sharp lower bounds for p-fundamental tone without boundary regularity assumptions.
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.
problem Proving rigidity for self-shrinkers and surfaces with parallel weighted mean curvature.
method Using a new generalization of Cauchy's Theorem in complex analysis.
result Rigidity results for self-shrinkers and surfaces with parallel weighted mean curvature.
Paper disproves Milnor's conjecture about manifold fundamental groups.
problem Milnor's conjecture about complete manifolds with nonnegative Ricci curvature.
method New topological construction and analysis of mapping class group.
result Found a counterexample with infinitely generated fundamental group.
New method improves PCA for high-dimensional data with n < p.
problem PCA struggles in high-dimensional settings with n < p.
method Pairwise differences covariance estimation with four regularized versions.
result Proposed methods outperform existing estimators in high-dimensional data settings.
Teaching tool simplifies Monte Carlo simulation for project risk analysis.
problem Difficulty in students performing Monte Carlo Simulation in risk analysis.
method Introducing MCSimulRisk as a teaching tool.
result Students can perform Monte Carlo simulation and apply it to projects of any complexity.
The book explores alternatives to worst-case analysis for algorithm performance.
problem Providing strong worst-case guarantees for many algorithms is impossible.
method Surveying and detailing various nuanced analysis approaches.
result More nuanced analysis approaches are needed for fundamental problems.