NeuralFactors uses deep learning to improve factor analysis in equity modeling.
problem Enhancing classical factor models for better risk forecasting and portfolio construction.
method Introduces a novel machine-learning approach (NeuralFactors) that outputs factor exposures and returns, trained using variational autoencoders.
result NeuralFactors outperforms prior approaches in log-likelihood performance and computational efficiency.
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.
Here we propose a novel model family with the objective of learning to disentangle the factors of variation in data. Our approach is based on the spike-and-slab restricted Boltzmann machine which we generalize to include higher-order interactions among multiple latent variables. Seen from a generative perspective, the …
Develops multifactor risk models for equities using various factors.
problem Building robust risk models for equities using different factors.
method Constructs multifactor risk models via style factors, principal components, and industry factors. Uses the Russian-doll risk model for short horizons.
result Generalizes heterotic risk model to include arbitrary non-industry factors.
N-VAE separates class-specific and shared factors in data.
problem Multimodal distribution of generative factors due to class distinction.
method N-VAE model with class-conditioned and shared latent spaces.
result Model effectively disentangles class-dependent and shared factors.
This paper proposes a model to learn multimodal representations robust to missing data.
problem Learning multimodal representations from heterogeneous sources of information.
method Optimizes a joint generative-discriminative objective across multimodal data and labels, factorizing representations into multimodal discriminative and modality-specific generative factors.
result The proposed model achieves state-of-the-art performance on six multimodal datasets and can reconstruct missing modalities without significant performance drop.
Generative model improves word embeddings with latent factors.
problem Incorporating latent factors into word embeddings.
method Generative model with low rank positive semidefinite approximation.
result Generative model outperforms existing methods on benchmark datasets.
Paper tackles deep learning confounding factors, learns unseen factors.
problem Learning from data with unknown and potentially infinite confounding factors.
method Combines deep generative models with Bayesian non-parametric factor models (Indian Buffet Process).
result Model can learn from data with unknown and potentially infinite confounding factors.
Hedonic models predict 84-92% of U.S. real estate prices, highlighting environmental factors' impact.
problem Predicting real estate prices using hedonic models with environmental factors.
method P-spline generalized additive models for real estate prices, contrasting with linear and polynomial models.
result GAM models explain 84-92% of U.S. real estate price variance, with environmental factors contributing minimally.
Proposes a model to generate high-dimensional financial returns using latent factor structure.
problem Challenges in financial scenario simulation, especially in high-dimensional and small data settings.
method Integrates latent factor structure into generative diffusion processes, decomposing the score function using time-varying orthogonal projections.
result Establishes rigorous statistical guarantees for score estimation and generated distribution, surpassing dimension-dependent limits.
Framework captures missing data in sparse data sets.
problem Capturing missing data in extremely sparse data sets.
method Coupled compound Poisson factorization with stochastic variational inference.
result Explicitly modeling missing data improves results in clustering, prediction, and matrix factorization.
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.
A new model Weighted-SVD improves recommendation accuracy by adjusting latent factor weights.
problem Current Matrix Factorization models assume equal weights for all latent factors, which may not be accurate.
method Integrates linear regression with SVD to allow different weights for latent factors.
result The Weighted-SVD model outperforms other models in RMSE metrics on multiple datasets.
Deep models improve factor analysis by capturing non-linearity and interaction effects.
problem Improving factor models to better capture complex asset relationships.
method Developed deep fundamental factor models with uncertainty quantification and hidden layers.
result Generated information ratios approximately 1.5x greater than traditional models.
Tensor variable elimination for plated factor graphs enables exact inference in models with repeated structure.
problem Efficient inference in models with repeated structure.
method Generalized variable elimination to tensor variable elimination on plated factor graphs.
result Tractable inference for a class of plated factor graphs.
Improved portfolio optimization using GAM factor models.
problem Enhancing CVaR portfolio optimization performance.
method Combines autoregressive filters with factor regressions to predict stock returns.
result Substantial improvement in portfolio performances with GAM models.
Mathematical models reveal key factors for engaging Gen Z at work.
problem Understanding and improving engagement of Generation Z employees.
method Correlation and cluster analyses on engagement surveys.
result Clear responsibilities and challenging work are essential for Gen Z employees.
FASC clusters data with latent factors, improving on naive methods.
problem Clustering high-dimensional data with correlated variables.
method Factor Adjusted Spectral Clustering (FASC) algorithm.
result FASC achieves an exponentially low mislabeling rate under general assumptions.
This study analyzes relationships between factor endowments and commodity outputs in a trade model.
problem Analyzing factor endowment-commodity output relationships in a trade model.
method Developed a method to estimate the position of the EWS-ratio vector and derived sufficient conditions for specific sign patterns.
result Derived sufficient conditions for extreme factors to be complements and for specific Stolper-Samuelson sign patterns.
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…
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.
Method ranks generative models without needing latent factor supervision.
problem Challenges in selecting generative models for qualities like disentanglement.
method Ranking generative models based on training dynamics, without requiring labels for latent factors.
result Method correlates with supervised disentanglement metrics and can predict downstream performance.
Integrates regression trees to explain latent factor models in recommendation systems.
problem Difficulty in explaining latent factor models in personalized recommendations.
method Builds regression trees on users and items using user-generated reviews to guide latent factor model learning and explain latent factors.
result Model generates explainable recommendations by tracking latent profiles through regression tree paths.
We found that factors decay over time, with momentum fitting best.
problem Understanding how factors decay over time and their impact on performance.
method Derived a hyperbolic decay model for factors, tested against linear and exponential alternatives.
result Momentum exhibits hyperbolic decay, outperforming linear and exponential models.
This paper diagnoses factor-model pricing errors using a new method.
problem Measuring pricing errors in factor models with general characteristic axes.
method Developed a method to measure factor-model pricing errors as bridge-alpha curves, using a predetermined characteristic order and prefix portfolios.
result Adding a counterpart factor flips the curve's sign on every axis, but only HML and CMA overcorrect enough to be rejected.
Survey of factor analysis, PCA, variational inference, and VAE.
problem Dimensionality reduction and generative modeling of data.
method Variational inference, factor analysis, probabilistic PCA, and VAE.
result Derivation and explanation of ELBO, EM, and closed-form solutions.
We propose a new IRT model that directly factors test items without factor analysis.
problem Existing multidimensional IRT methods require factorization, which is posthoc and linear.
method We use a sparsity-promoting horseshoe prior to factorize items directly within the IRT model.
result Our model performs factorization directly and consistently selects the correct number of factors.
Deep model learns complex latent codes without assuming factor structure.
problem Learning latent codes with complex, non-factorial distributions.
method Deep generative factor analysis with beta process prior and stochastic EM algorithm.
result Preliminary results show model can approximate complex distributions.
The paper explores indeterminacy in latent factor projections and its implications for data representation.
problem Indeterminacy in latent factor projections and its implications for data representation.
method Analyzes the fundamental problem of indeterminacy in latent factor projections and discusses its implications for data representation.
result Latent factor determinacy across all facets is achieved when the feature-dimension grows to infinity.
New insights into how deep models generalize, focusing on matrix factorization.
problem Understanding how deep models generalize and why they work well.
method Using Morse functions and dynamical systems to study implicit regularization.
result Solved a conjecture on implicit regularization in matrix factorization.
Paper proposes an alternating back-propagation algorithm for generator networks.
problem Learning realistic generator models of natural images, video sequences, and sounds.
method Alternating back-propagation algorithm that iterates inferential and learning steps.
result The alternating back-propagation algorithm can learn realistic generator models of natural images, video sequences, and sounds.
New portfolios outperform traditional methods by using factor weights.
problem Improving portfolio allocation in markets driven by factors.
method Factor-weighted Dirichlet portfolios outperform uniform Dirichlet portfolios.
result Factor-weighted portfolios outperform uniformly sampled portfolios in market returns.
New algorithm improves asset pricing model for high-dimensional financial data.
problem Estimating high-dimensional financial data with many risk-factors.
method Groupwise Interpretable Basis Selection (GIBS) algorithm for adaptive multi-factor model.
result AMF model outperforms Fama-French 5-factor model in fitting and prediction.
FAVAE learns disentangled representations from sequential data.
problem Learning disentangled and interpretable representations from sequential data.
method FAVAE uses the information bottleneck principle without supervision.
result FAVAE can disentangle multiple dynamic factors.
Probabilistic inference in graphical models is the task of computing marginal and conditional densities of interest from a factorized representation of a joint probability distribution. Inference algorithms such as variable elimination and belief propagation take advantage of constraints embedded in this factorization …
GRU-PFG model extracts inter-stock correlations from stock factors using graph neural networks.
problem Limited effectiveness of models relying solely on stock factors for capturing stock correlations.
method Project stock factors into a graph and use graph neural networks to extract inter-stock correlations.
result Achieves better prediction results than models relying solely on stock factors and comparable to second category models.
The paper develops a new model for high-dimensional spatial arbitrage pricing.
problem Estimating spatial interactions in high-dimensional asset pricing.
method Integrates spatial interactions with multi-factor analysis using generalized shrinkage Yule-Walker (SYW) estimation.
result Established asymptotic properties for high-dimensional spatial arbitrage pricing models.
Discond-VAE separates continuous and discrete factors in data.
problem Separating shared and class-specific variations in real-world data.
method Introduces private and public latent variables to represent continuous and discrete factors, respectively.
result Discond-VAE successfully disentangles class-dependent continuous factors from discrete factors.
Optimizes Bayesian priors for matrix factorization without posterior inference.
problem Selecting optimal priors for Bayesian models in machine learning.
method Prior predictive distribution and virtual statistics matching user-provided or observed data statistics.
result Analytically determines hyperparameters for Poisson factorization models.
Improves prediction performance on biological data by controlling confounding factors.
problem Challenges in statistical learning due to confounding variables in biological data.
method ONION for removing confounding covariates and DANN for penalizing confounder information.
result Significant improvements in generalization performance on simulated and empirical patient data.
IZF uses flow-based models to overcome ZSL limitations.
problem Hardness of training ZSL models and limited generation quality.
method IZF incorporates flow-based models to learn factorized data embeddings and generates samples.
result IZF significantly outperforms existing methods on ZSL benchmarks.
Dual model predicts electricity spot prices using neural networks and wavelets.
problem Forecasting hourly electricity spot prices.
method Dual generalized long memory modelling with k-factor GARMA and G-GARCH models, using LLWNN and PSO for variance prediction.
result The hybrid k-factor GARMA-LLWNN model outperforms other methods in forecasting accuracy.
Unified framework for generating data by modeling causal and correlational dependencies.
problem Modeling both causal and correlational dependencies among latent factors.
method Causal-Correlation Variational Autoencoder (C2VAE) framework.
result Improves generation quality, disentanglement, and intervention fidelity.
A new method for non-negative matrix factorization using generalized dual divergence.
problem Non-negative matrix factorization for various noise structures.
method Theoretical framework based on generalized dual Kullback-Leibler divergence, with algorithms developed and proven convergence using Expectation-Maximization.
result Generalizes existing methods and provides an alternative for non-negative matrix factorizations.
Estimates multi-attribute choice preferences using private signals and matrix factorization.
problem Modeling multi-attribute choice preferences under weak assumptions.
method Generative choice model with latent factor matrices and private signals; multi-stage matrix factorization.
result Validated estimation performance of novel algorithm through simulations.
Paper tackles cold start problem in recommendation systems using deep learning and latent factor models.
problem Cold start problem in recommendation systems for new users and items.
method Uses model-based approach and deep learning, specifically latent factor model and convolutional neural network.
result Significantly outperforms baseline estimators in experiments.
New method reduces diffusion model function evaluations for discrete data.
problem High computational burden in generating samples from masked diffusion models.
method Modified causal attention mask and speculative sampling mechanism for non-factorized predictions.
result Achieved ~2x reduction in required network forward passes.
Matching only the marginal distribution of latent style variables in factorized models fails to prevent class leakage.
problem Class leakage in factorized generative models despite matching marginal distributions.
method Derive an exact decomposition showing four conditions required for factorized sampling, and demonstrate that matching only the marginal distribution is insufficient.
result Class labels can be recovered with high accuracy (74%--100%) from factorized generative models, indicating leakage.