Paper establishes DRL for high-dimensional rewards.
problem Intractable reinforcement learning with high-dimensional rewards.
method Theoretical foundations and a novel DRL algorithm.
result Bellman operator contraction in high-dimensional spaces.
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48 results for “high-dimensional returns”
MarketGAN generates financial returns using GANs to match empirical stylized facts.
problem Generating financial returns under data scarcity and preserving stylized facts.
method Generative adversarial learning with a TCN backbone.
result MarketGAN outperforms conventional methods in portfolio applications.
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.
PS^2 selects assets then weights for high-dimensional investing.
problem High-dimensional mean--variance investing challenges.
method Two-step framework: Lasso screening followed by standard portfolio estimation.
result FPS^2 with defactored returns improves performance.
Optimizes high-dimensional portfolios using joint shrinkage.
problem Optimizing portfolios with many assets where classical methods fail.
method Regression-based joint shrinkage method for estimating partial correlations.
result Superior performance in variance, weight, and risk estimation compared to other methods.
Cluster GARCHecon.EM
Cluster GARCH model improves multivariate GARCH for high-dimensional asset returns.
problem Modeling high-dimensional asset returns with flexible tail dependencies and cluster structures.
method Introduced a novel multivariate GARCH model with flexible convolution-t distributions, tractable likelihood and derivatives for dynamic correlation structure.
result Cluster GARCH model outperforms existing models in daily returns of 100 assets, both in-sample and out-of-sample.
Sparse APCA identifies sparse factors in financial returns over time.
problem Analyzing co-movements of high-dimensional panel data over time.
method Sparse asymptotic PCA with truncated power method for sparse factors and sequential deflation for multi-factor cases.
result Identification of nine risk factors influencing the S&P 500 stock market.
A new model optimizes portfolios by learning stock return distributions conditioned on factors.
problem Optimizing portfolios with high-dimensional asset-specific factors.
method Conditional Diffusion Transformer architecture linking each asset's return to its factor vector.
result The model outperforms benchmarks in mean-variance and mean-CVaR optimization.
New shrinkage estimator for GMV portfolio reduces risk in high-dimensional asset settings.
problem Estimating the global minimum variance portfolio in high-dimensional settings with limited data.
method Dynamic shrinkage of the GMV portfolio using previous data as a target.
result The new estimator outperforms traditional methods in high-dimensional asset settings.
Deep RL algorithm trades high-dimensional stock portfolios.
problem Trading high-dimensional stock portfolios with data gaps and non-unique history lengths.
method Deep Q-learning algorithm, sequentially setting up environments, rewarding based on asset returns and cash reservation.
result Algorithm outperforms all passive and active benchmarks by a large margin.
New model uses financial news to predict stock returns.
problem Predicting stock returns based on financial news.
method Derive company embedding vectors from news, select basis assets, and use statistical methods.
result NEUS model outperforms Fama-French 5-factor model.
Split-Session Cluster GARCH for Overnight and Intraday Returns: The Role of Tail Heterogeneityecon.EM
The Split-Session Cluster GARCH model captures tail heterogeneity in overnight and intraday returns.
problem Capturing tail behavior and dependence in multivariate asset returns.
method Convolution- distributions, session and sector clustering, block-structured correlation matrices.
result Session-specific and sector-level tail parameters improve model fit and out-of-sample performance.
This paper proposes an embedding-based neural network for more accurate investment return prediction.
problem Accurately predicting investment returns requires understanding industry knowledge and news, as well as leveraging relevant theories.
method The approach uses embedding to encode investment IDs into low-dimensional vectors, leveraging dual branches to separate different information, and employs the swish activation function.
result The proposed embedding-based dual branch model outperforms traditional machine learning models like Xgboost, Lightgbm, and Catboost on the Ubiquant Market Prediction dataset.
Cross-sectional "Information Coefficient" (IC) is a widely and deeply accepted measure in portfolio management. The paper gives an insight into IC in view of high-dimensional directional statistics: IC is a linear operator on the components of a centralizing-unitizing standardized random vector of next-period cross-sec…
Hybridizes CEM and gradient descent for efficient model-predictive control.
problem Efficiently planning optimal action sequences in high-dimensional spaces.
method Interleaves Cross-Entropy Method (CEM) and gradient descent steps.
result Faster convergence and avoidance of local optima compared to CEM.
CB-APM uses analyst consensus as a bottleneck to interpret stock returns.
problem Tackles the challenge of understanding and predicting stock returns using professional beliefs.
method Embeds analyst consensus as a structural bottleneck, treating it as a sufficient statistic for market information.
result CB-APM portfolios exhibit strong monotonic return gradients and robust across different economic conditions.
Enhances feature augmentation for high-dimensional learning.
problem Correlated high-dimensional measurements require dimensionality reduction.
method Augment features with factors extracted from design matrices and their transformations.
result Significantly weakens correlations between input variables, improving interpretability and numerical stability.
In this study, we develop a deterministic nonlinear filtering algorithm based on a high-dimensional version of Kitagawa (1987) to evaluate the likelihood function of models that allow for stochastic volatility and jumps whose arrival intensity is also stochastic. We show numerically that the deterministic filtering met…
Unified framework for estimating high-dimensional conditional factor models.
problem Estimating high-dimensional conditional latent factor models with practical limitations.
method Constrained nuclear norm regularization and cross-validation for parameter selection.
result Imposing homogeneity improves model predictability, with new method outperforming alternatives.
This paper investigates how to measure common market risk factors using newly proposed Panel Quantile Regression Model for Returns. By exploring the fact that volatility crosses all quantiles of the return distribution and using penalized fixed effects estimator we are able to control for otherwise unobserved heterogen…
Hybrid GARCH-LSTM models predict covariance matrices better than GARCH alone.
problem Predicting covariance matrices of high-dimensional asset returns.
method Combining GARCH processes with neural networks to forecast volatilities and correlations.
result The hybrid model outperforms both equally weighted portfolios and univariate GARCH models.
Based on iterative optimization and activation function in deep learning, we proposed a new analytical framework of high-frequency trading information, that reduced structural loss in the assembly of Volume-synchronized probability of Informed Trading (), Generalized Autoregressive Conditional Heteroscedasticity …
This paper forecasts cryptocurrency log-returns using LASSO-VAR and sentiment analysis.
problem Forecasting log-returns of cryptocurrencies using social media sentiment.
method LASSO-VAR model combined with Twitter and Reddit sentiment data.
result The model predicts the correct direction of cryptocurrency returns more than 50% of the time.
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systemsstat.ML
We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogat…
Study improves stock return uncertainty prediction using Gaussian mixture distributions.
problem Improving prediction of stock market return uncertainty.
method Gaussian mixture distribution-based deep learning model.
result Superior performance in volatility estimation, especially during market volatility.
Develops a dynamic latent-factor model for high-dimensional asset characteristics.
problem Estimating asset pricing tests with high-dimensional data.
method Dynamic latent-factor model with Double Selection Lasso regularization.
result The inflation-mimicking portfolio in the crypto asset class has positive risk compensation.
A new method finds diverse near-optimal portfolios using quality-diversity.
problem Optimizing financial portfolios with robustness to input parameter uncertainties.
method Quality-Diversity (QD) optimization using CVT-MAP-Elites algorithm.
result Diverse set of near-optimal portfolios identified.
New methods improve portfolio risk minimization by estimating covariance matrix more accurately.
problem Uncertainty in estimating covariance matrix leads to unreliable hedge trades.
method Proposes two new estimators of the inverse covariance matrix using l2 and l1 norms.
result Portfolio formed using proposed estimators achieves substantial risk reduction and improved returns.
Our article considers a Gaussian variational approximation of the posterior density in a high-dimensional state space model. The variational parameters to be optimized are the mean vector and the covariance matrix of the approximation. The number of parameters in the covariance matrix grows as the square of the number …
P-Trees improve investment performance by optimizing the efficient frontier.
problem Optimizing investment performance in complex financial markets.
method Introducing P-Trees, a new tree-based model for analyzing panel data.
result P-Trees significantly advance the efficient frontier and outperform existing models.
Two semimetrics on probability distributions are proposed, given as the sum of differences of expectations of analytic functions evaluated at spatial or frequency locations (i.e, features). The features are chosen so as to maximize the distinguishability of the distributions, by optimizing a lower bound on test power f…
GATSBI uses GANs for SBI, improving posterior estimation in high dimensions.
problem Statistical inference on stochastic models without likelihoods.
method Adversarial approach to variational objective, amortized inference, implicit priors.
result GATSBI returns well-calibrated posterior estimates in high dimensions.
A new estimator corrects bias in high-dimensional predictive regressions.
problem Bias in high-dimensional predictive regressions.
method IVX-desparsified LASSO (XDlasso) estimator.
result Corrects both shrinkage and Stambaugh bias.
Machine learning helps estimate risk premiums of stocks without knowing their factors.
problem Estimate risk premiums of stocks without knowing their underlying factors.
method Used elastic-net machine learning to project stock returns onto peers and construct replicate portfolios.
result Unique stocks have higher SARP and excess returns than ubiquitous stocks.
The paper develops methods to estimate the high-dimensional efficient frontier without distributional assumptions.
problem Estimating the mean-variance efficient frontier in high-dimensional settings.
method Random matrix theory and asymptotic analysis for high-dimensional data.
result Developed consistent estimators for the mean, variance, and covariance of the efficient frontier.
Paper tackles DOCTR-L with SciPhy RL, solving neural PDEs from data.
problem High-dimensional optimal control with stochastic policies.
method Soft HJB equation, Neural PDEs, Physics-Informed Neural Networks.
result Reduces DOCTR-L to solving neural PDEs from data.
In this article we consider the problem of pricing and hedging high-dimensional Asian basket options by Quasi-Monte Carlo simulation. We assume a Black-Scholes market with time-dependent volatilities and show how to compute the deltas by the aid of the Malliavin Calculus, extending the procedure employed by Montero and…
Practical reinforcement learning problems are often formulated as constrained Markov decision process (CMDP) problems, in which the agent has to maximize the expected return while satisfying a set of prescribed safety constraints. In this study, we propose a novel simulator-based method to approximately solve a CMDP pr…
New framework tests mean-variance spanning in high dimensions.
problem Testing mean-variance spanning in high-dimensional asset spaces.
method Robust Student-t statistic based on batch-mean method, combined using Cauchy combination test.
result Advantages of diversification vary by economic conditions and cross-country.
Scientists and engineers rely on accurate mathematical models to quantify the objects of their studies, which are often high-dimensional. Unfortunately, high-dimensional models are inherently difficult, i.e. when observations are sparse or expensive to determine. One way to address this problem is to approximate the or…
Paper proposes a deep learning method for better covariance matrix forecasting.
problem Suboptimal predictive performance in traditional matrix volatility forecasting.
method Riemannian-geometry-aware deep learning framework for symmetric positive definite matrices.
result Our method outperforms traditional approaches in predictive accuracy.
The global minimum-variance portfolio is a typical choice for investors because of its simplicity and broad applicability. Although it requires only one input, namely the covariance matrix of asset returns, estimating the optimal solution remains a challenge. In the presence of high-dimensionality in the data, the samp…
A Bayesian GED-Gamma stochastic volatility model for return data: a marginal likelihood approachq-fin.ST
Several studies explore inferences based on stochastic volatility (SV) models, taking into account the stylized facts of return data. The common problem is that the latent parameters of many volatility models are high-dimensional and analytically intractable, which means inferences require approximations using, for exa…
Empirical researchers are increasingly faced with rich data sets containing many controls or instrumental variables, making it essential to choose an appropriate approach to variable selection. In this paper, we provide results for valid inference after post- or orthogonal -Boosting is used for variable selection.…
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low s…
We estimate the global minimum variance (GMV) portfolio in the high-dimensional case using results from random matrix theory. This approach leads to a shrinkage-type estimator which is distribution-free and it is optimal in the sense of minimizing the out-of-sample variance. Its asymptotic properties are investigated a…
Extended study improves covariance matrix estimation for portfolio managers.
problem Limited sample sizes and poor performance of PCA estimator in high-dimensional returns.
method Developed a more general shrinkage framework targeting further information.
result Improves the PCA estimator of beta by shrinking it toward a target.
A neural network approach solves dynamic portfolio optimization without dynamic programming.
problem Dynamic portfolio optimization with multiple constraints and high rebalancing frequency.
method Parsimonious neural network without dynamic programming, avoiding high-dimensional expectations.
result Proves convergence to theoretical optimal solution under general conditions.