Model learns aging rates from single cross-sectional data.
problem Cross-sectional data limits traditional time-series methods.
method Latent-variable model with order-isomorphic nonlinear function.
result Reconstructs aging rates from single cross-sectional data.
TQA improves prediction intervals for time series data by adjusting quantiles for both cross-sectional and longitudinal coverage.
problem Constructing reliable prediction intervals for cross-sectional time series data.
method Temporal Quantile Adjustment (TQA) method that adjusts the quantile in Conformal Prediction to account for both cross-sectional and longitudinal coverage.
result TQA improves longitudinal coverage while preserving cross-sectional coverage, as validated through extensive experimentation.
CPTD improves prediction intervals in time series regression with cross-sectional data.
problem Constructing valid prediction intervals in time series regression with a cross-section.
method Conformal Prediction with Temporal Dependence (CPTD) for post-hoc, light-weight approach.
result CPTD maintains cross-sectional validity while improving longitudinal coverage.
Proposes Equity2Vec for cross-sectional asset pricing.
problem Sub-optimal performance due to missing cross-sectional effects and heterogeneous data.
method End-to-end deep learning framework with Equity2Vec for graph-based interactions and all alpha sources.
result Outperforms state-of-the-art approaches in real-world stock market datasets.
Paper uses cross-sectional data to estimate VSL, reducing confidence intervals by a factor of 3.
problem Estimating VSL with cross-sectional data using Garen's IV approach.
method Garen's instrumental variable (IV) approach, proxy for risk attitude.
result Confidence intervals reduced by a factor of 3 using proxy.
Machine learning portfolios perform well with simple imputation of missing data.
problem Handling missing values in machine learning portfolios constructed from cross-sectional return predictors.
method Simple imputation with cross-sectional means compared to rigorous expectation-maximization methods.
result Simple imputation performs well due to the structure of missing data.
Set-Sequence model learns cross-sectional dynamics directly from time series data.
problem Predicting large cross-sections of time series data with latent cross-sectional dynamics.
method A model that learns cross-sectional structure directly, enhancing expressivity and eliminating manual feature engineering.
result Significantly outperforms strong baselines in equity portfolio optimization and loan risk prediction.
Pipeline integrates cross-sectional and longitudinal multi-omics data for IBD research.
problem Integrating diverse data types from the same individuals for disease understanding.
method Statistical and deep learning methods for variable selection, feature extraction, and joint integration.
result Identified microbial pathways, metabolites, and genes discriminating IBD status.
LPCI provides valid prediction intervals for longitudinal data.
problem Current conformal prediction methods for time series data lack cross-sectional coverage when applied to longitudinal datasets.
method Modeling residual data as a quantile fixed-effects regression problem, constructing prediction intervals with a trained quantile regressor.
result LPCI achieves valid cross-sectional coverage and outperforms existing benchmarks in terms of longitudinal coverage rates.
New method tests Granger non-causality in panel data with cross-sectional dependencies.
problem Testing Granger non-causality in panel data with cross-sectional dependencies.
method Proposes a new approach to aggregate p-values from panel members to test Granger non-causality, showing lower FDR.
result Our approach discovers true causal relations in panel data, unlike state-of-the-art methods.
Bayesian analysis of geological shapes for improved oil prediction.
problem Simplistic classifications of geological shapes hinder accurate oil prediction.
method Deriving integrated likelihood for cross-sectional shapes given class parameters using Bayesian statistics.
result First coherent statistical analysis of geological shapes.
Study shows nontrivial links as cross-sections of unknotted holomorphic disks.
problem Resolving parts of Kirby's list problem about nontrivial links.
method Using unknotted holomorphic disks in the four-ball to produce cross-sections.
result Many nontrivial links arise as cross-sections of unknotted holomorphic disks.
Study shows different types of volatility and skewness changes affect stock prices.
problem Different types of volatility and skewness changes affect stock prices.
method Used intraday data for individual stocks to analyze cross-section of asset returns.
result Idiosyncratic transitory and persistent shocks to volatility and skewness are priced differently in stock returns.
Improved nuclear cross section fitting with weighted Levenberg-Marquardt method.
problem Challenging optimization in multichannel nuclear cross section data.
method Weighted Levenberg-Marquardt algorithm with Fisher Information Metric.
result More physically consistent fits for raw and smoothed datasets.
The paper defines cross-section continuity for angular momentum definitions and finds the CWY definition valid.
problem Defining angular momentum at null infinity and ensuring its continuity across different cross-sections.
method Introducing cross-section continuity as a criterion and proving it for specific angular momentum definitions.
result The Chen-Wang-Yau definition of angular momentum satisfies cross-section continuity, while the Compere-Nichols modification does not.
New method estimates spatial weights matrix for lattice data, improving prediction accuracy.
problem Estimating spatial dependence structure for regular lattice data.
method Adaptive lasso with cross-sectional resampling to estimate sparse spatial weights matrix.
result Improves prediction accuracy of nitrogen dioxide concentrations.
The study identifies flat manifolds with unique cusp cross-sections in arithmetic hyperbolic manifolds.
problem Characterizing flat manifolds that have unique cusp cross-sections in arithmetic hyperbolic manifolds.
method Algebraic characterization of cusp cross-sections in arithmetic hyperbolic manifolds.
result Construction of flat manifolds with unique cusp cross-sections and proof of their existence in all dimensions n≥32. New algorithm improves asset ranking for better cross-sectional portfolios.
problem Sub-optimal ranking of assets in cross-sectional systematic strategies.
method Learning-to-rank algorithms to enhance portfolio construction.
result Modern machine learning ranking algorithms boost Sharpe Ratios by approximately threefold.
Conditions for flat manifolds as cusp cross-sections in arithmetic hyperbolic manifolds.
problem Determining when a flat manifold can be a cusp cross-section in arithmetic hyperbolic manifolds.
method Analyzing rational representations of holonomy groups and quasi-arithmetic manifolds.
result Conditions for a flat manifold to appear as a cusp cross-section in every commensurability class of arithmetic hyperbolic manifolds.
The paper evaluates forecast accuracy of realized volatility measures in large cross-sections.
problem Forecast evaluation of realized volatility measures in large cross-sections of financial data.
method Equal predictive accuracy testing procedures, LASSO shrinkage, measurement error correction, cross-sectional jump component measures.
result The augmented HAR model outperforms the standard HAR model in forecasting realized volatility.
A diagnostic tool for identifying approximate factor structures in equity datasets.
problem Detecting approximate factor structures in large cross-sectional equity datasets.
method Computes the largest eigenvalue of the empirical cross-sectional covariance matrix of residuals.
result Validates the presence of weak cross-sectional correlation or shared unobservable common factors.
Unified model learns from both time-series and cross-sectional momentum features.
problem Separate time-series and cross-sectional momentum strategies do not consider concurrent relationships.
method Spatio-Temporal Momentum strategies using neural networks to combine both types of momentum.
result Simple neural network with single fully connected layer generates trading signals for all assets.
Paper finds conditions for finite-dimensional vector spaces of cross-sections.
problem Finite-dimensional vector spaces of cross-sections for holomorphic bundles.
method Analyzes complex vector spaces of holomorphic cross-sections over elliptic orbits.
result Provides a sufficient condition for vector spaces to be finite dimensional.
Study on stability of surfaces in null cones under area-preserving variations.
problem Investigating stability of spacelike cross sections of null cones.
method Area-preserving variations, Hawking energy analysis, spherical cross sections.
result Only round spheres are stable cross sections of the standard Minkowski lightcone.
Estimates mean and covariance for large, unbalanced stock returns panels.
problem Estimating mean and covariance in large, unbalanced panel data.
method Nonparametric, kernel-based joint estimator for conditional mean and covariance matrices.
result The idiosyncratic risk explains more than 75% of cross-sectional variance.
This study proves energy bounds in specific AdS spacetimes.
problem Proving positive energy theorems in asymptotically locally AdS spacetimes.
method Derived positive energy theorem for spacetimes with compact, Einstein cross-sections.
result First complete proofs of BPS inequalities in AdS and locally AdS spacetimes.
The study identifies factors predicting stock returns and maximum drawdown using various models.
problem Predicting stock returns and maximum drawdown in the US equity market.
method Supervised learning with multiple models (OLS, penalized linear regressions, tree-based models, neural networks) over 49 years of data.
result Non-linear models outperformed linear models in predicting stock returns and maximum drawdown, especially during calm periods.
Motivated by a question of Hirzebruch on the possible topological types of cusp cross-sections of Hilbert modular varieties, we give a necessary and sufficient condition for a manifold M to be diffeomorphic to a cusp cross-section of a Hilbert modular variety. Specialized to Hilbert modular surfaces, this proves that e…
Norms on curves on surfaces classify Birkhoff cross sections.
problem Classifying Birkhoff cross sections on surfaces.
method Defining norms on homology groups and interpreting integer points.
result Integer points in dual unit balls classify isotopy classes of Birkhoff cross sections.
Paper develops a new estimator for high-dimensional panel data with common shocks.
problem Cross-sectionally dependent errors driven by common shocks in high-dimensional panel data.
method Factor-augmented sparse-group LASSO estimator combining MIDAS aggregation with latent factors.
result The estimator outperforms standard LASSO for prediction and estimation in settings with cross-sectional dependence.
Deep learning predicts cross-sectional stock prices for practical investment.
problem Predicting stock prices using cross-sectional factors.
method Deep learning model for daily stock price prediction.
result Profitable investment framework demonstrated in Japanese stock market.
Classifies Nil 3-manifolds as cross-sections of complex hyperbolic surfaces.
problem Identifying Nil 3-manifolds as cross-sections of complex hyperbolic surfaces.
method Comprehensive classification of commensurability classes of cusped, arithmetic, and non-arithmetic complex hyperbolic 2-manifolds.
result Some Nil 3-manifolds are cross-sections in every commensurability class, while others are cross-sections in only one.
Factor models for cryptoasset returns identified a key contributor.
problem Understanding the cross-section of daily cryptoasset returns.
method Proposed factor models, provided source code for data and computations.
result Identified a leading factor contributing to cryptoasset returns.
Simple bounds show most cross-sectional predictability findings are likely true.
problem Determining the validity of cross-sectional return predictability findings.
method Developed simple and intuitive bounds on the false discovery rate (FDR).
result Bounds show the FDR is small, indicating most findings are likely true.
Paper uses HPCA for better stock correlation modeling.
problem Challenges in modeling cross-sectional correlations between thousands of stocks.
method Hierarchical Principal Component Analysis (HPCA) and statistical clustering.
result HPCA provides better cross-sectional correlations than classic PCA.
PRISM-VQ combines financial priors with vector quantization for better stock prediction.
problem Predicting cross-sectional stock returns is hard due to low signal-to-noise ratios and changing market conditions.
method Integrates expert priors, vector-quantized latent factors, and dynamic factor loadings.
result Consistent improvements in cross-sectional return prediction and portfolio performance.
Peer-reviewed research and mined data predict stock returns similarly.
problem Predicting stock returns using research quality.
method Cross-sectional analysis of 29,000 accounting ratios with t-statistics > 2.0.
result Post-sample performance is largely independent of whether the predictor is peer-reviewed or mined.
EIDGM model estimates DE parameters from RCS data.
problem Estimating DE parameters from RCS data with heterogeneities.
method Physics-informed neural network emulator + Wasserstein GAN parameter generator.
result EIDGM accurately captures diverse parameter distributions.
Optimal transport learns Riemannian metrics for evolving probability measures.
problem Learning metrics for evolving probability measures on Riemannian manifolds.
method Neural parametrization of a metric tensor via optimal transport, alternating optimization scheme.
result Improved trajectory inference on scRNA and bird migration data.
Deep learning predicts stock returns better than shallow networks.
problem Predicting stock returns in the cross-section.
method Deep learning applied to neural networks for stock return prediction.
result Deep neural networks outperform shallow neural networks and other models.
The present paper is devoted to some results concerning with the complete lifts of an almost complex structure and a connection in a manifold to its (0,q)-tensor bundle along the corresponding cross-section.
Weyl's tube formula holds for various cross-sections under symmetry conditions.
problem Can the volume of tubes around submanifolds be calculated for non-round cross-sections?
method Investigated the volume of tubes with general cross-sections D under symmetry conditions.
result The volume of tubes around submanifolds can be calculated for general cross-sections under symmetry conditions.
The main purpose of present paper is to study the affine connection induced from the horizontal lift on the cross-section determined by a vector field in Mn with respect to the adapte frame of .
A new model explains asset returns with a single factor, improving cross-sectional performance.
problem Understanding the cross-section of asset returns with complex models.
method Proposes a non-linear single-factor asset pricing model with a nonparametric link function estimated jointly with sieve-based estimators.
result The model delivers superior cross-sectional performance with a low-dimensional approximation of the link function.
Neural networks improve efficiency in integrating multi-dimensional phase spaces in particle physics.
problem Efficiently integrating multi-dimensional phase spaces in particle physics.
method Optimized Neural Network (NN) algorithm for phase space integration.
result NN-based approach achieves unweighting efficiencies of 30-75% in various particle physics examples.
Study uses CSIE to estimate portfolio volatility relative to market.
problem Estimating relative volatility risk of stock portfolios.
method Cross-sectional intrinsic entropy (CSIE) model to estimate cross-sectional volatility.
result Discover sets of symbols that outperform market indices in terms of return with similar or lower risk.
New method controls false discoveries in financial asset pricing.
problem Controlling false discoveries in time series with unknown correlations.
method Double bootstrapping method to control false discovery rate.
result Superior statistical power and controlled false discovery rate.
We present a new simple method of estimating stochastic volatility and its volatility. This method is applicable to both cross-sectional and time-series data. Moreover, this method does not require volatility data series.