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Diffusion Factor Models: Generating High-Dimensional Returns with Factor Structure

https://doi.org/10.2139/ssrn.5211437
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The 87 checked references that resolve
resolves10.1146/annurev-financial-110921-101555
Climate Stress Testing
resolves10.1111/jofi.12189
Financial Intermediaries and the Cross‐Section of Asset Returns
resolves10.1080/01621459.2017.1401542
Principal Component Analysis of High-Frequency Data
resolves10.1093/jjfinec/nbi003
The Present and Future of Financial Risk Management
resolves10.1016/0304-4149(82)90051-5
Reverse-time diffusion equation models
resolves10.1146/annurev-financial-120209-133947
Bayesian Portfolio Analysis
resolves10.1111/joes.12532
Asset Pricing and Machine Learning: A critical review
resolves10.1111/1468-0262.00273
Determining the Number of Factors in Approximate Factor Models
resolves10.1016/j.jeconom.2023.01.027
Approximate factor models with weaker loadings
resolves10.1007/978-3-319-00227-9
Analysis and Geometry of Markov Diffusion Operators
resolves10.1016/0893-6080(89)90014-2
Neural networks and principal component analysis: Learning from examples without local minima
resolves10.1073/pnas.1907378117
Benign overfitting in linear regression
resolves10.1111/jofi.13124
The Limits of Model‐Based Regulation
resolves10.1016/j.orl.2003.12.007
Robust linear optimization under general norms
resolves10.1214/009053607000000758
Regularized estimation of large covariance matrices
resolves10.1146/annurev-financial-110311-101754
A Survey of Systemic Risk Analytics
resolves10.1016/j.jmva.2018.07.004
Optimal shrinkage estimator for high-dimensional mean vector
resolves10.1080/07350015.2021.2004897
Optimal Shrinkage-Based Portfolio Selection in High Dimensions
resolves10.1016/j.cviu.2018.10.009
Pros and cons of GAN evaluation measures
resolves10.1145/3559540
Generative Adversarial Networks in Time Series: A Systematic Literature Review
resolves10.1016/j.jfineco.2021.12.007
A factor model for option returns
resolves10.1111/j.1540-6261.1997.tb03808.x
On Persistence in Mutual Fund Performance
resolves10.2307/1912275
Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets
resolves10.1287/mnsc.2023.4695
Deep Learning in Asset Pricing
resolves10.1093/imaiai/iaac001
Nonparametric regression on low-dimensional manifolds using deep ReLU networks: function approximation and statistical recovery
resolves10.1093/nsr/nwae348
Opportunities and challenges of diffusion models for generative AI
resolves10.1086/296344
Economic Forces and the Stock Market
resolves10.1145/3604237.3626854
Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness
resolves10.3982/ECTA7432
Efficient Semiparametric Estimation of the Fama-French Model and Extensions
resolves10.1080/713665670
Empirical properties of asset returns: stylized facts and statistical issues
resolves10.1109/MSP.2017.2765202
Generative Adversarial Networks: An Overview
resolves10.1137/0707001
The Rotation of Eigenvectors by a Perturbation. III
resolves10.1287/mnsc.1080.0986
A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms
resolves10.1093/rfs/hhm075
Optimal Versus Naive Diversification: How Inefficient is the 1/ <i>N</i> Portfolio Strategy?
resolves10.1287/mnsc.2023.4784
A One-Factor Model of Corporate Bond Premia
resolves10.1016/0304-405X(93)90023-5
Common risk factors in the returns on stocks and bonds
resolves10.1257/0895330042162430
The Capital Asset Pricing Model: Theory and Evidence
resolves10.1016/j.jfineco.2014.10.010
A five-factor asset pricing model
resolves10.1080/01621459.2020.1825448
Estimating Number of Factors by Adjusted Eigenvalues Thresholding
resolves10.1111/ectj.12061
An overview of the estimation of large covariance and precision matrices
resolves10.1111/rssb.12016
Large Covariance Estimation by Thresholding Principal Orthogonal Complements
resolves10.1111/jofi.12883
Taming the Factor Zoo: A Test of New Factors
resolves10.1017/S0022109023000893
Deep Learning in Characteristics-Sorted Factor Models
resolves10.1146/annurev-financial-101521-104735
Factor Models, Machine Learning, and Asset Pricing
resolves10.1093/rfs/hhaa111
Thousands of Alpha Tests
resolves10.1086/714090
Asset Pricing with Omitted Factors
resolves10.1111/jofi.13415
Test Assets and Weak Factors
resolves10.1007/s10287-011-0130-2
On the role of norm constraints in portfolio selection
resolves10.1007/s10994-020-05929-w
Regularisation of neural networks by enforcing Lipschitz continuity
resolves10.1093/rfs/hhaa009
Empirical Asset Pricing via Machine Learning
resolves10.1016/j.jeconom.2020.07.009
Autoencoder asset pricing models
resolves10.1109/TKDE.2021.3130191
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
resolves10.1111/mafi.12382
Recent advances in reinforcement learning in finance
resolves10.1093/rfs/hhv059
… and the Cross-Section of Expected Returns
resolves10.1146/annurev-statistics-030718-105122
Risk Measures: Robustness, Elicitability, and Backtesting
resolves10.1016/j.jfineco.2017.08.002
Intermediary asset pricing: New evidence from many asset classes
resolves10.1109/TIT.2025.3557050
Convergence Analysis of Probability Flow ODE for Score-Based Generative Models
resolves10.1080/14697688.2023.2205583
A generative model of a limit order book using recurrent neural networks
resolves10.1111/1540-6261.00580
Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps
resolves10.1111/j.1540-6261.1993.tb04702.x
Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
resolves10.2307/2331042
Bayes-Stein Estimation for Portfolio Analysis
resolves10.1017/S0022109000004129
Optimal Portfolio Choice with Parameter Uncertainty
resolves10.1111/jofi.13199
Principal Portfolios
resolves10.1561/500000064
Financial Machine Learning
resolves10.1016/j.jfineco.2019.05.001
Characteristics are covariances: A unified model of risk and return
resolves10.1016/S0927-5398(03)00007-0
Improved estimation of the covariance matrix of stock returns with an application to portfolio selection
resolves10.1016/S0047-259X(03)00096-4
A well-conditioned estimator for large-dimensional covariance matrices
resolves10.1093/jjfinec/nbaa007
The Power of (Non-)Linear Shrinking: A Review and Guide to Covariance Matrix Estimation
resolves10.1111/0022-1082.00347
Consumption, Aggregate Wealth, and Expected Stock Returns
resolves10.1016/j.jeconom.2019.08.012
Estimating latent asset-pricing factors
resolves10.1093/rfs/hhaa020
Factors That Fit the Time Series and Cross-Section of Stock Returns
resolves10.1111/mafi.12423
Sig‐Wasserstein GANs for conditional time series generation
resolves10.1111/jofi.13119
Common Risk Factors in Cryptocurrency
resolves10.1146/annurev-financial-110112-121009
Empirical Cross-Sectional Asset Pricing
resolves10.1162/REST_a_00043
Determining the Number of Factors from Empirical Distribution of Eigenvalues
resolves10.1086/374184
Liquidity Risk and Expected Stock Returns
resolves10.1093/rfs/hhz064
Testing Beta-Pricing Models Using Large Cross-Sections
resolves10.1111/mafi.12365
Deep empirical risk minimization in finance: Looking into the future
resolves10.1093/rof/rfac027
Bank Stress Testing: Public Interest or Regulatory Capture?
resolves10.1214/25-SS152
Score-based diffusion models via stochastic differential equations
resolves10.1017/S0022109010000335
Incorporating Economic Objectives into Bayesian Priors: Portfolio Choice under Parameter Uncertainty
resolves10.1162/NECO_a_00142
A Connection Between Score Matching and Denoising Autoencoders
resolves10.1080/14697688.2023.2299466
Fin-GAN: forecasting and classifying financial time series via generative adversarial networks
resolves10.1145/3626235
Diffusion Models: A Comprehensive Survey of Methods and Applications
resolves10.1016/j.neunet.2017.07.002
Error bounds for approximations with deep ReLU networks
resolves10.1111/j.1540-6261.2006.00848.x
A Consumption‐Based Explanation of Expected Stock Returns
resolves10.1007/s002450010003
Continuous-Time Mean-Variance Portfolio Selection: A Stochastic LQ Framework
The 93 references without a DOI — listed, not checked
no DOI — not checkedref1
no DOI — not checkedref5
no DOI — not checkedref8
no DOI — not checkedref10
no DOI — not checkedSigdiffusions: Score-based diffusion models for long time series via log-signature embeddings
no DOI — not checkedSpectrally-normalized margin bounds for neural networks
no DOI — not checkedNearly d-linear convergence bounds for diffusion models via stochastic localization
no DOI — not checkedAsset-pricing factors with economic targets
no DOI — not checkedA survey on generative diffusion models
no DOI — not checkedref32
no DOI — not checkedEvolution of gaussian concentration bounds under diffusions
no DOI — not checkedMarginalized denoising autoencoders for domain adaptation
no DOI — not checked2023a, Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data
no DOI — not checkedOn generalization bounds of a family of recurrent neural networks
no DOI — not checkedref40
no DOI — not checked2023b, Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
no DOI — not checkedRestoration-degradation beyond linear diffusions: A nonasymptotic analysis for ddim-type samplers
no DOI — not checkedScore-based generative models break the curse of dimensionality in learning a family of sub-gaussian probability distributions
no DOI — not checkedOn the constrained time-series generation problem
no DOI — not checkedLimit order book simulation with generative adversarial networks
no DOI — not checkedref51
no DOI — not checkedThe usual suspects? reassessing blame for vae posterior collapse
no DOI — not checkedref55
no DOI — not checkedDiffusion schr�dinger bridge with applications to score-based generative modeling
no DOI — not checkedDiffusion models beat gans on image synthesis
no DOI — not checkedref60
no DOI — not checkedref61
no DOI — not checkedref63
no DOI — not checkedProjected principal component analysis in factor models
no DOI — not checkedNonlinear time series: nonparametric and parametric methods
no DOI — not checkedThe variation of economic risk premiums
no DOI — not checkedThe lottery ticket hypothesis: Finding sparse, trainable neural networks
no DOI — not checkedref77
no DOI — not checkedWasserstein convergence guarantees for a general class of scorebased generative models
no DOI — not checkedref79
no DOI — not checkedref89
no DOI — not checkedDeep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
no DOI — not checkedNeural network-based score estimation in diffusion models: Optimization and generalization
no DOI — not checkedref94
no DOI — not checkedLagging inference networks and posterior collapse in variational autoencoders
no DOI — not checkedDenoising diffusion probabilistic models
no DOI — not checkedSparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
no DOI — not checkedElbo surgery: yet another way to carve up the variational evidence lower bound
no DOI — not checkedref101
no DOI — not checkedEstimation of non-normalized statistical models by score matching
no DOI — not checkedref105
no DOI — not checkedThe conditional capm and the cross-section of expected returns
no DOI — not checkedref111
no DOI — not checkedElucidating the design space of diffusion-based generative models
no DOI — not checkedModeling corporate bond returns
no DOI — not checkedStatistical efficiency of score matching: The view from isoperimetry
no DOI — not checkedTabddpm: Modelling tabular data with diffusion models
no DOI — not checkedConvergence for score-based generative modeling with polynomial complexity
no DOI — not checkedConvergence of score-based generative modeling for general data distributions
no DOI — not checked2024a, Towards non-asymptotic convergence for diffusion-based generative models
no DOI — not checkedref128
no DOI — not checkedUnified convergence analysis for score-based diffusion models with deterministic samplers
no DOI — not checkedUnderstanding generalizability of diffusion models requires rethinking the hidden gaussian structure
no DOI — not checkedref131
no DOI — not checkedref133
no DOI — not checkedChallenging common assumptions in the unsupervised learning of disentangled representations
no DOI — not checkedLearning sparse neural networks through l 0 regularization
no DOI — not checkedref137
no DOI — not checkedImproved denoising diffusion probabilistic models
no DOI — not checkedDiffusion models are minimax optimal distribution estimators
no DOI — not checkedref145
no DOI — not checkedU-net: Convolutional networks for biomedical image segmentation
no DOI — not checkedref147
no DOI — not checkedref148
no DOI — not checkedref150
no DOI — not checkedGenerative modeling by estimating gradients of the data distribution
no DOI — not checkedImproved techniques for training score-based generative models
no DOI — not checkedSliced score matching: A scalable approach to density and score estimation
no DOI — not checkedref154
no DOI — not checkedThe implicit bias of gradient descent on separable data
no DOI — not checkedTraining sparse neural networks
no DOI — not checkedAdaptivity of diffusion models to manifold structures
no DOI — not checkedref158
no DOI — not checkedCsdi: Conditional score-based diffusion models for probabilistic time series imputation
no DOI — not checkedref161
no DOI — not checkedref162
no DOI — not checkedThe future of data analysis
no DOI — not checkedref165
no DOI — not checkedVOLGAN: A generative model for arbitrage-free implied volatility surfaces
no DOI — not checkedref169
no DOI — not checkedref170
no DOI — not checkedThe diffusion process as a correlation machine: Linear denoising insights
no DOI — not checkedOptimal score estimation via empirical bayes smoothing
no DOI — not checkedTackling the generative learning trilemma with denoising diffusion gans
no DOI — not checkedGeneralization error bound for denoising score matching under relaxed manifold assumption
no DOI — not checkedConvergence in kl and r�nyi divergence of the unadjusted langevin algorithm using estimated score
no DOI — not checkedTime-series generative adversarial networks
no DOI — not checkedMinimax optimality of score-based diffusion models: Beyond the density lower bound assumptions
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