Academic

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

arXiv:2603.19288v1 Announce Type: cross 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, t

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Keonvin Park · · 1 min read · 6 views
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arXiv:2603.19288v1 Announce Type: cross 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.

Executive Summary

This article presents a novel joint return and risk modeling framework utilizing deep neural networks for portfolio construction. The proposed model learns dynamic expected returns and risk structures from sequential financial data, outperforming traditional allocation approaches in terms of risk-adjusted performance. The model achieves competitive predictive accuracy and captures volatility clustering and regime shifts. The Neural Portfolio strategy integrated with the proposed model demonstrates superior performance compared to equal weight and historical mean-variance benchmarks. The framework offers a scalable and practical alternative for data-driven portfolio construction under nonstationary market conditions.

Key Points

  • ▸ The article proposes a joint return and risk modeling framework based on deep neural networks.
  • ▸ The model learns dynamic expected returns and risk structures from sequential financial data.
  • ▸ The proposed model outperforms traditional allocation approaches in terms of risk-adjusted performance.

Merits

Strength in Capturing Volatility Clustering

The model effectively captures volatility clustering and regime shifts, which is a significant improvement over traditional allocation approaches.

Scalable and Practical Framework

The proposed framework offers a scalable and practical alternative for data-driven portfolio construction under nonstationary market conditions.

Competitive Predictive Accuracy

The model achieves competitive predictive accuracy (RMSE = 0.0264) with economically meaningful directional accuracy (51.9%).

Demerits

Limited Generalizability

The model's performance may not generalize to other asset classes or market conditions.

Dependence on High-Quality Data

The model's performance relies heavily on the quality and quantity of the input data.

Lack of Explainability

The model's decision-making process may be difficult to interpret and explain.

Expert Commentary

The article presents a novel and promising approach to portfolio construction and risk management using deep neural networks. The model's ability to capture volatility clustering and regime shifts is particularly noteworthy. However, the limitations of the model, such as limited generalizability and dependence on high-quality data, need to be addressed. Additionally, the lack of explainability of the model's decision-making process is a concern. Nevertheless, the article's contributions to the field of machine learning in finance and portfolio optimization are significant, and the proposed framework has the potential to improve portfolio performance and risk management in practice.

Recommendations

  • ✓ Further research is needed to address the limitations of the model and improve its generalizability and explainability.
  • ✓ The proposed framework should be tested on other asset classes and market conditions to assess its robustness and performance.

Sources

Original: arXiv - cs.AI

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