Introduces m-connecting imset and factorization for ADMG models.
problem Handling latent confounding in DAG models.
method Introduces m-connecting imset and m-connecting factorization criterion for ADMG models.
result Equivalence of m-connecting factorization criterion to global Markov property.
We show that for an m-connected cell complex X the space exp_k X of non-empty subsets of X of cardinality at most k is (m + k - 2)-connected
In this paper, we provide new discrete uniformization theorems for bounded, m-connected planar domains. To this end, we consider a planar, bounded, m-connected domain Ω and let $\bordΩ$ be its boundary. Let T denote a triangulation of $Ω\cup\bordΩ$. We construct a \emph{new} decomposition of $Ω\cup\bo…
We extend our discrete uniformization theorems for planar, m-connected, Jordan domains [Journal für die reine und angewandte Mathematik 670 (2012), 65--92] to closed surfaces of non-positive genus.
For n >2, we shall show that the group Aut(NS(M)) of simplicial automorphisms of the complex NS(M) of non-separating embedded spheres in the manifold M,connected sum of n copies of S^2 X S^1, isomorphic to the group Out(F_n) of outer automorphisms of the free group F_n, where Fn is identified with the fundamental gr…
Consider a planar, bounded, m-connected region Ω, and let $\bordΩ$ be its boundary. Let T be a cellular decomposition of $Ω\cup\bordΩ$, where each 2-cell is either a triangle or a quadrilateral. From these data and a conductance function we construct a canonical pair (S,f) where S is a genus (m−1)…
Connectedness of small clusters in Riemannian and Finsler manifolds proven.
problem Understanding connectedness of small clusters in Riemannian and Finsler manifolds.
method Proved connectedness and small diameter properties for clusters of small volume in both manifolds.
result Clusters in Riemannian manifolds are connected and have small diameter; in Finsler manifolds, they are at most m connected components of small diameter.
The paper defines Hesse solitons and explores their properties on Hessian manifolds.
problem Exploring self-similar solutions to the Hesse flow on Hessian manifolds.
method Defining Hesse solitons and analyzing their properties on Hessian manifolds.
result Compact proper Hesse solitons are expanding, and non-trivial compact gradient Hesse solitons are proper.
This paper is concerned with lower bounds for the connectivity of graphs (one-dimensional skeleta) of triangulations of compact manifolds. We introduce a structural invariant b_M for simplicial d-manifolds M taking values in the range 0 <= b_M <= d-1. The main result is that b_M influences connectivity in the following…
In this paper we continue the study started in part I (posted). We consider a planar, bounded, m-connected region Ω, and let $\bordΩ$ be its boundary. Let T be a cellular decomposition of $Ω\cup\bordΩ$, where each 2-cell is either a triangle or a quadrilateral. From these data and a conductance function…
The kth finite subset space of a topological space X is the space exp_k X of non-empty subsets of X of size at most k, topologised as a quotient of X^k. Using results from our earlier paper (math.GT/0210315) on the finite subset spaces of connected graphs we show that the kth finite subset space of a connected cell com…
The paper explores quantum statistical manifolds and their autoparallelity, providing estimation-theoretical characterizations.
problem Quantum statistical manifolds and their geometric properties.
method Study of autoparallelity w.r.t. the e-connection, using quantum estimation theory.
result Characterizations of e-autoparallel submanifolds as statistical models with efficient estimators.
The paper establishes bounds on the lengths of geodesics on manifolds with curvature constraints.
problem Finding bounds on the lengths of geodesics on manifolds with curvature constraints.
method Using rational functions and homotopy theory, the paper establishes bounds on the lengths of geodesics.
result There exist at least m geodesics connecting p and q of length at most m*exp(c*exp(G(n,k,v,D))).
Develops a deep multi-factor model for factor investing with clear financial insights.
problem Lack of interpretability and unclear financial insights in non-linear factor models.
method Industry and market neutralization modules, graph attention modules, factor-attention module.
result Demonstrates effectiveness in factor investing with real-world stock market data.
Factor Engine simplifies financial factor computation and analysis in Python.
problem Efficient computation and analysis of financial factors.
method Modular, extensible Python library with decorators, integrates with data science ecosystem.
result Mispricing factors computed by Factor Engine and Stata implementation are highly similar.
New risk factors improve stress testing accuracy.
problem Improving stress testing accuracy with new risk factors.
method Adapted PCA and autoencoders for dimension reduction and interpretation.
result Aggregated risk factors enhance stress testing outcomes.
New statistical factors improve portfolio risk estimation.
problem Improving estimation of portfolio risk using new statistical factors.
method Matrix factor models and statistical methods (partial F test, double selection LASSO).
result New statistical factors add explanatory power in asset pricing.
Introduces factor risk measures to assess risk relative to multiple factors.
problem Measuring risk relative to multiple factors.
method Introduces a double-argument mapping as a risk measure to assess risk relative to a vector of factors.
result Characterizes various types of factor risk measures including distortion, quantile, linear, and coherent measures.
AlphaLogics mines market logic to generate interpretable alpha factors.
problem Complex, opaque alpha factors from factor mining overlook market logic.
method Market Logic Mining, Factor Generation and Optimization, Market Logic Generation and Optimization.
result AlphaLogics improves predictive metrics and risk-adjusted returns over baselines.
Method learns shared and specific factors in multi-study gene expression data.
problem Understanding shared and specific factors in high-dimensional multi-study data.
method Nonlinear multi-study factor model with sparse variational autoencoder.
result Method recovers meaningful shared and specific factors in platelet gene expression data.
FactorGCL uses hypergraph learning to predict stock returns by mining hidden factors.
problem Mining effective factors in data-driven models is challenging due to low signal-to-noise ratio in market data.
method FactorGCL employs a hypergraph structure and temporal residual contrastive learning to extract hidden factors.
result FactorGCL outperforms existing methods and mines effective hidden factors for predicting stock returns.
We propose a nonparametric Bayesian factor regression model that accounts for uncertainty in the number of factors, and the relationship between factors. To accomplish this, we propose a sparse variant of the Indian Buffet Process and couple this with a hierarchical model over factors, based on Kingman's coalescent. We…
We present a novel factor analysis method that can be applied to the discovery of common factors shared among trajectories in multivariate time series data. These factors satisfy a precedence-ordering property: certain factors are recruited only after some other factors are activated. Precedence-ordering arise in appli…
The paper derives a formula for factorizing categorical data to improve Bayes classifiers.
problem Improving the accuracy of Bayes classifiers by effectively factoring multidimensional data.
method Derives an explicit formula for calculating the marginal likelihood of a factorized categorical dataset.
result The derived formula can be used to select the best factorization for constructing a Bayes classifier.
We introduce Bayesian multi-tensor factorization, a model that is the first Bayesian formulation for joint factorization of multiple matrices and tensors. The research problem generalizes the joint matrix-tensor factorization problem to arbitrary sets of tensors of any depth, including matrices, can be interpreted as u…
We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor …
A study finds that only a few factors explain corporate bond risk, rendering extensive bond factor literature redundant.
problem The redundancy of extensive bond factor literature in explaining corporate bond risk premia.
method Bayesian Model Averaging Stochastic Discount Factor analysis of 18 quadrillion models.
result A Bayesian Model Averaging SDF explains risk premia better than low-dimensional models, with an out-of-sample Sharpe ratio of 1.5 to 1.8.
Large language models improve futures market factor models in China.
problem Designing effective factor models for Chinese futures markets.
method Used large language models (GPT) to generate 40 factors for single and multi-factor portfolios.
result GPT-generated factors outperform benchmarks with high Sharpe ratios and alphas.
New tests for identifying the number of latent factors in short panels with small time dimensions.
problem Determining the number of latent factors in short panels with small time dimensions.
method Eigenvalue tests based on variance-covariance matrices of asset returns, with assumptions on spherical errors or instrumental variables for factor betas.
result Established asymptotic distributional results and proposed a novel statistical test for weak factors.
Optimal tensor PCA for estimating factors and loadings in high-dimensional panel data.
problem Estimating factors and loadings in high-dimensional panel data with non-negligible correlations.
method Tensor Principal Component Analysis (TPCA) for estimating factors and loadings in a tensor factor model.
result Simple TPCA is optimal for strong factors and can be improved for weak factors with alternating least-squares iterations.
Paper proposes NNAFC for automatic financial factor construction.
problem Manual factor construction is time-consuming and prone to bias.
method NNAFC uses neural networks to automatically construct diversified financial factors.
result NNAFC outperforms GP in constructing more informative and diversified factors.
We give a simple explicit algorithm for building multi-factor risk models. It dramatically reduces the number of or altogether eliminates the risk factors for which the factor covariance matrix needs to be computed. This is achieved via a nested "Russian-doll" embedding: the factor covariance matrix itself is modeled v…
AlphaForge mines and dynamically combines alpha factors for better investment performance.
problem Inconsistency and inflexibility of fixed factor weights in alpha factor mining.
method Generative-predictive neural network for factor generation and dynamic weight adjustment.
result Demonstrated superior performance in formulaic alpha factor mining and portfolio returns.
Sparse GFA identifies disease factors in FTD subgroups.
problem Heterogeneity in neurological disorders hinders understanding and treatment.
method Sparse Group Factor Analysis (GFA) with regularised horseshoe priors.
result Identified latent disease factors differentially expressed in FTD subgroups.
The paper tests stock return models and uses LSTM to predict stock returns.
problem Validating stock return models and predicting stock returns.
method Used Fama-French three-factor, four-factor, and five-factor models; also used LSTM model.
result Fama-French five-factor model shows better validity for stock returns.
Green stocks show less factor exposure heterogeneity compared to brown stocks.
problem Exploring differences in factor exposure between green and brown stocks.
method Examined S&P 500 firms grouped by greenhouse gas emissions, analyzing factor exposure over 2014-2020.
result Green stocks have less factor exposure heterogeneity than brown stocks, except for the value factor.
ATLAS separates invariant and transferable latent factors across diverse environments.
problem Transfer learning and robust prediction in heterogeneous environments.
method ATLAS leverages invariance principle to disentangle latent factors and uses auxiliary labels for robust prediction.
result Near-oracle performance and robust transferable prediction in new environments.
In this letter, we propose a new identification criterion that guarantees the recovery of the low-rank latent factors in the nonnegative matrix factorization (NMF) model, under mild conditions. Specifically, using the proposed criterion, it suffices to identify the latent factors if the rows of one factor are \emph{suf…
New model explains low-volatility anomaly using adaptive multi-factor approach.
problem Explaining the low-volatility anomaly in stock markets.
method Used Adaptive Multi-Factor (AMF) model with GIBS algorithm to identify significant risk factors.
result Low-volatility portfolios perform better due to loaded risk factors, not just low volatility.
Study tests if equity factors explain Bitcoin's risk and returns.
problem Explaining Bitcoin's risk and return with equity factors.
method Applied statistical methods to test Fama-French factors on Bitcoin's excess returns.
result Fama-French factors have explanatory power on Bitcoin's risk and returns.
Corporate bond factor research is flawed due to measurement errors and ex-post filtering.
problem Replication crisis in corporate bond factor research.
method Analysis of 108 signals across nine thematic clusters, correction of transaction prices and return filtering.
result Majority of previously documented factors do not produce statistically significant alphas after correction.
We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical …
The MAXFLAT low-pass filter improves factor adjustment for better portfolio performance in China's stock market.
problem Improving factor adjustment for better portfolio performance in China's stock market.
method Using MAXFLAT low-pass volatility model to adjust factors and construct portfolios.
result Adjusted factors by MAXFLAT volatility model show better performance in both large and small cap universes.
The article studies factorization structures in geometry and their applications to cones and polytopes.
problem Understanding and characterizing factorization structures in geometry.
method Comprehensive study of factorization structures, including structure theory, construction of compatible polytopes and cones, and derivation of generalised Gale's evenness condition.
result Established generalised Vandermonde identities and found examples of Delzant and rational Delzant compatible polytopes.
The Matrix Factorization models, sometimes called the latent factor models, are a family of methods in the recommender system research area to (1) generate the latent factors for the users and the items and (2) predict users' ratings on items based on their latent factors. However, current Matrix Factorization models p…
In a very high-dimensional vector space, two randomly-chosen vectors are almost orthogonal with high probability. Starting from this observation, we develop a statistical factor model, the random factor model, in which factors are chosen at random based on the random projection method. Randomness of factors has the con…
This study examines the evolving causal structure of equity risk factors.
problem Redundancy and risk contagion in multi-factor strategies during financial crises.
method Causal structure learning methods applied to US equity market data over 29 years.
result Statistically significant sparsifying trend of causal structure during normal times, but densification during financial stress.
Knockoffs method selects financial factors, controlling false discoveries.
problem Controlling false discoveries in financial factor selection.
method Apply knockoff procedure to build fake factors.
result Shows versatility in fund replication and network inference.