Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction

• URL: http://arxiv.org/abs/2603.19288v1
• Date: Mon, 09 Mar 2026 01:49:51 GMT
• Title: Joint Return and Risk Modeling with Deep Neural Networks for Portfolio Construction
• Abstract summary: This paper proposes a joint return and risk modeling framework based on deep neural networks.<n>Using daily data from ten large-cap US equities spanning 2010 to 2024, the proposed model is evaluated across return prediction, risk estimation, and portfolio-level performance.<n>The framework offers a scalable and practical alternative for data-driven portfolio construction under nonstationary market conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Portfolio construction traditionally relies on separately estimating expected returns and covariance matrices using historical statistics, often leading to suboptimal allocation under time-varying market conditions. This paper proposes a joint return and risk modeling framework based on deep neural networks that enables end-to-end learning of dynamic expected returns and risk structures from sequential financial data. Using daily data from ten large-cap US equities spanning 2010 to 2024, the proposed model is evaluated across return prediction, risk estimation, and portfolio-level performance. Out-of-sample results during 2020 to 2024 show that the deep forecasting model achieves competitive predictive accuracy (RMSE = 0.0264) with economically meaningful directional accuracy (51.9%). More importantly, the learned representation effectively captures volatility clustering and regime shifts. When integrated into portfolio optimization, the proposed Neural Portfolio strategy achieves an annual return of 36.4% and a Sharpe ratio of 0.91, outperforming equal weight and historical mean-variance benchmarks in terms of risk-adjusted performance. These findings demonstrate that jointly modeling return and covariance dynamics can provide consistent improvements over traditional allocation approaches. The framework offers a scalable and practical alternative for data-driven portfolio construction under nonstationary market conditions.

Related papers

• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• Enhancing a Risk Model by Adding Transient Statistical Factors [90.40844650665713]
  Estimating asset returns is a key component of financial portfolio construction and evaluation.<n>We propose a systematic method based on maximum likelihood estimation to enhance an existing factor model.<n>We show that the proposed extension captures structure in the returns that is missed by the original model.
  arXiv  Detail & Related papers  (2026-05-13T04:15:24Z)
• $V_{0.5}$: Generalist Value Model as a Prior for Sparse RL Rollouts [81.48669089692189]
  Generalist Value Models (such as $V_0.5$) achieve pre-trained value estimation by explicitly encoding model capabilities in-context.<n>We propose $V_0.5$, which adaptively fuses the baseline predicted by such value model with the empirical mean derived from sparse rollouts.<n>$V_0.5$ significantly outperforms GRPO and DAPO, achieving faster convergence and over some 10% performance improvement.
  arXiv  Detail & Related papers  (2026-03-11T14:57:41Z)
• Regression Models Meet Foundation Models: A Hybrid-AI Approach to Practical Electricity Price Forecasting [39.91788164419845]
  We propose FutureBoosting, a novel paradigm that enhances regression-based forecasts.<n>We instantiate this paradigm into a lightweight, plug-and-play framework for electricity price forecasting.<n>Our framework consistently outperforms state-of-the-art TSFMs and regression baselines.
  arXiv  Detail & Related papers  (2026-03-06T02:06:46Z)
• The GT-Score: A Robust Objective Function for Reducing Overfitting in Data-Driven Trading Strategies [51.56484100374058]
  GT-Score is a composite objective function that integrates performance, statistical significance, consistency, and downside risk.<n>In walk-forward validation, GT-Score improves the generalization ratio by 98% relative to baseline objective functions.<n>These results suggest that embedding an anti-overfitting structure into the objective can improve the reliability of backtests in quantitative research.
  arXiv  Detail & Related papers  (2026-01-22T05:16:47Z)
• Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning [41.52380204321823]
  This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning.<n>We evaluate UAMDP in two domains-high-frequency equity trading and retail inventory control-both marked by structural uncertainty and economic volatility.
  arXiv  Detail & Related papers  (2025-10-09T13:46:32Z)
• Dependency Network-Based Portfolio Design with Forecasting and VaR Constraints [8.107171581224312]
  This study proposes a novel portfolio optimization framework that integrates statistical social network analysis with time series forecasting and risk management.<n>Using daily stock data from the S&P 500 ( 2020-2024), we construct dependency networks via Vector Autoregression ( VAR) and Forecast Error Variance Decomposition (FEVD)<n>FEVD breaks down the VAR's forecast error variance to quantify how much each stock's shocks contribute to another's uncertainty information we invert to form influence-based edge weights in our network.<n>A dynamic portfolio is constructed using the top-ranked stocks, with capital allocated based on Value at Risk (
  arXiv  Detail & Related papers  (2025-07-26T18:53:39Z)
• End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning [0.0]
  We develop a rotation-invariant neural network that provides the global minimum-variance portfolio.<n>This explicit mathematical mapping offers clear interpretability of each module's role.<n>A single model can be calibrated on panels of a few hundred stocks and applied, without retraining, to one thousand US equities.
  arXiv  Detail & Related papers  (2025-07-02T17:27:29Z)
• Conformal Predictive Portfolio Selection [10.470114319701576]
  We propose a framework for predictive portfolio selection via conformal prediction.<n>Our approach forecasts future portfolio returns, computes the corresponding prediction intervals, and selects the portfolio of interest based on these intervals.<n>We demonstrate the effectiveness of the CPPS framework by applying it to an AR model and validate its performance through empirical studies.
  arXiv  Detail & Related papers  (2024-10-19T15:42:49Z)
• Mean-Variance Portfolio Selection in Long-Term Investments with Unknown Distribution: Online Estimation, Risk Aversion under Ambiguity, and Universality of Algorithms [0.0]
  This paper adopts a perspective where data gradually and continuously reveal over time.<n>The original model is recast into an online learning framework, which is free from any statistical assumptions.<n>When the distribution of future data follows a normal shape, the growth rate of wealth is shown to increase by lifting the portfolio along the efficient frontier through the calibration of risk aversion.
  arXiv  Detail & Related papers  (2024-06-19T12:11:42Z)
• Bayesian Nonparametrics Meets Data-Driven Distributionally Robust Optimization [29.24821214671497]
  Training machine learning and statistical models often involve optimizing a data-driven risk criterion. We propose a novel robust criterion by combining insights from Bayesian nonparametric (i.e., Dirichlet process) theory and a recent decision-theoretic model of smooth ambiguity-averse preferences. For practical implementation, we propose and study tractable approximations of the criterion based on well-known Dirichlet process representations.
  arXiv  Detail & Related papers  (2024-01-28T21:19:15Z)
• DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions [53.37679435230207]
  We propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data.
  arXiv  Detail & Related papers  (2022-09-23T16:13:47Z)
• On the benefits of maximum likelihood estimation for Regression and Forecasting [35.386189585135334]
  We advocate for a practical Maximum Likelihood Estimation (MLE) approach for regression and forecasting. This approach is better suited to capture inductive biases such as prior domain knowledge in datasets. We demonstrate empirically that our method instantiated with a well-designed general purpose mixture likelihood family can obtain superior performance over Empirical Risk Minimization.
  arXiv  Detail & Related papers  (2021-06-18T22:10:43Z)
• Deep Learning for Portfolio Optimization [5.833272638548154]
  Instead of selecting individual assets, we trade Exchange-Traded Funds (ETFs) of market indices to form a portfolio. We compare our method with a wide range of algorithms with results showing that our model obtains the best performance over the testing period.
  arXiv  Detail & Related papers  (2020-05-27T21:28:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.