Paper: arXiv 2603.19288

Authors: Keonvin Park

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.

Complexity vs Empirical Score

  • Math Complexity: 3.5/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper attempts to present an end-to-end LSTM framework for portfolio optimization, but the methodology relies on basic rolling window statistics for risk rather than true dynamic deep covariance modeling as claimed. Additionally, the empirical evaluation is limited to a small 10-asset universe without accounting for realistic transaction costs or turnover constraints.

Research Flowchart

  flowchart TD
    Goal["Research Goal: Joint Return & Risk Modeling <br> for Dynamic Portfolio Construction"] --> Input["Data Inputs: Daily US Large-Cap Equities <br> (10 Stocks, 2010–2024)"]
    Input --> Method["Deep Neural Network Architecture <br> (End-to-End Joint Learning)"]
    Method --> CompProc["Computational Process: Forecast Returns <br> & Estimate Covariance Matrix Dynamics"]
    CompProc --> PortOpt["Portfolio Optimization <br> (Neural Portfolio Strategy)"]
    PortOpt --> Outcomes["Key Outcomes (2020–2024 Test): <br> • Annual Return: 36.4%, Sharpe: 0.91 <br> • RMSE: 0.0264, Directional Acc: 51.9% <br> • Captures Volatility Clustering & Regimes <br> • Outperforms Equal Weight & Mean-Variance"]

Cited References (30)

  1. Portfolio selection (1952)
  2. A new interpretation of information rate (1956)
  3. Lifetime portfolio selection under uncertainty (1969)
  4. Autoregressive conditional heteroscedasticity (1982)
  5. Generalized autoregressive conditional heteroskedasticity (1986)
  6. ImageNet classification with deep convolutional neural networks (2012)
  7. Long short-term memory (1997)
  8. Attention is all you need (2017)
and 22 more
  1. Learning long-term dependencies with gradient descent (1994)
  2. Deep learning for finance (2019)
  3. Classification-based financial markets prediction using deep neural networks (2017)
  4. Deep learning for portfolio optimization (2020)
  5. Deep learning and optimal portfolio selection (2020) — link
  6. End-to-end risk budgeting portfolio optimization with neural networks (2021) — link
  7. A universal end-to-end approach to portfolio optimization via deep learning (2021) — link
  8. Time-series forecasting with deep learning: A survey (2021)
  9. Universal features of price formation in financial markets (2019)
  10. Deep learning for trading and portfolio optimization (2021)
  11. Using deep learning to detect price change indications in limit order books (2017)
  12. Empirical asset pricing via machine learning (2020)
  13. Deep learning with long short-term memory networks for financial market predictions (2018)
  14. Learning to trade via direct reinforcement (2001)
  15. Reinforcement learning for optimized trade execution (2006)
  16. Portfolio optimization with deep learning (2022)
  17. Volatility clustering in financial markets (2001)
  18. Options, Futures, and Other Derivatives (2018)
  19. A well-conditioned estimator for large-dimensional covariance matrices (2004)
  20. Agent-based computational finance (2006)
  21. Risk reduction in large portfolios (2003)
  22. Convex Optimization (2004)

0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.