Meta-Optimized Risk-Aware Portfolio Management: A Hybrid Deep Reinforcement Learning and LSTM-GRU Ensemble

Majid Sorouri; Deniz NoorMohammadzadehMaleki; Arman Salehi; Amirfarhad Farhadi; Azadeh Zamanifar

doi:10.1109/SEEDA-CECNSM68644.2025.11329752

Research question

How can forecasting, policy learning, and downside-risk control be coordinated for sequential portfolio allocation?

Condensed abstract

The paper presents a hybrid architecture combining an LSTM–GRU forecasting ensemble, deep reinforcement-learning agents, and meta-optimization for risk-aware portfolio decisions.

Key contribution

A forecasting-and-control architecture centered on explicit downside-risk objectives and adaptive optimization.

Method overview

Sequence forecasting, deep reinforcement learning, a CVaR-oriented objective, and meta-optimization are combined in a portfolio-management pipeline.

Interactive method figure

Forecast, act, then inspect the tail

01Market sequence
02LSTM–GRU forecast
03DRL policy
04Risk objective
05Allocation

Synthetic ordered losses

Worst-case tail 20%

VaR threshold: 25
Illustrative CVaR: 29.5

The architecture follows the paper’s method description. The ordered losses are synthetic and explain VaR and CVaR; they are not reported backtest results.

Dataset and evaluation

Data

Financial-market data as described in the conference paper. No public data package is linked from this portfolio.

Protocol

Backtesting and Bayesian analysis are reported in the proceedings paper. The portfolio does not add experimental detail that cannot be checked against the full record.

Main results

The proceedings paper reports quantitative backtesting results; this site intentionally does not restate point estimates that have not been independently verified from the full paper.

Baseline comparison

No broad superiority claim is made. Conclusions remain conditional on the paper’s market period, cost assumptions, baselines, and backtesting protocol.

Limitations and failure modes

Financial backtests are sensitive to market regime, transaction costs, leakage, and selection effects.
No release-grade reproduction package is linked from the public portfolio.
The work is methodological and must not be interpreted as investment advice.

Reproducibility resources

No public reproduction package is linked from this portfolio.

Verification sources

Cite this work

Download .bib Download .ris

Majid Sorouri, Deniz NoorMohammadzadehMaleki, Arman Salehi, Amirfarhad Farhadi, Azadeh Zamanifar (2025). Meta-Optimized Risk-Aware Portfolio Management: A Hybrid Deep Reinforcement Learning and LSTM-GRU Ensemble. 2025 10th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 1–6. https://doi.org/10.1109/SEEDA-CECNSM68644.2025.11329752

BibTeX

@inproceedings{sorouri2025riskaware,
  title = {Meta-Optimized Risk-Aware Portfolio Management: A Hybrid Deep Reinforcement Learning and LSTM-GRU Ensemble},
  author = {Majid Sorouri and Deniz NoorMohammadzadehMaleki and Arman Salehi and Amirfarhad Farhadi and Azadeh Zamanifar},
  year = {2025},
  doi = {10.1109/SEEDA-CECNSM68644.2025.11329752},
  booktitle = {2025 10th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM)},
  pages = {1--6},
  publisher = {IEEE}
}

Publication record last verified 2026-07-26.